Science topic

Comorbidity - Science topic

The presence of co-existing or additional diseases with reference to an initial diagnosis or with reference to the index condition that is the subject of study. Comorbidity may affect the ability of affected individuals to function and also their survival; it may be used as a prognostic indicator for length of hospital stay, cost factors, and outcome or survival.
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We're conducting a research design as follow:
  • An observational longitudinal study
  • Time period: 5 years
  • Myocardial infarction (MI) patients without prior heart failure are recruited (we'll name this number of people after 5 years of conducting our study A)
  • Exclusion criteria: Death during MI hospitalization or no data for following up for 3-6 months after discharge.
  • Outcome/endpoint: heart failure post MI (confirmed by an ejection fraction (EF) < 40%)
  • These patients will then be followed up for a period of 3 to maximum 6 months. If their EF during this 3-6 months after discharge is <40% -> they are considered to have heart failure post MI. (we'll name this number of people after 5 years of conducting our study B)
  • Otherwise they are not considered to have the aforementioned outcome/endpoint.
My question is as follow:
  1. What is the A/B best called? Is it cumulative incidence? We're well-aware of similar studies to ours but the one main different is they did not limit the follow up time (i.e: a patient can be considered to have heart failure post MI even 4 years after they were recruited). I wonder if this factor limits the ability to calculate cumulative incidence in our study?
  2. Is there a more appropriate measure to describe what we're looking to measure? How can we calculate incidence in this study?
  3. We also wanted to find associated factors (risk factor?) with heart failure post-MI. We collected some data about the MI's characteristics, the patients' comorbidities during the MI hospitalization (when they were first recruited). Can we use Cox proportional hazards model to calculate the HR of these factors?
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Hi,
The study starts with a Cohort A and on Follow up if Ef<40 then it will be in Group B. This Shift suggests that the survival decreases (Failure to be in Group A) i.e Survival Analysis is applicable. Since factors affecting the survival would be examined, then Cox Proportional Hazards Model is applicable. Survival curves are cumulative curves.
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What proportion of the US population has at least one or more comorbidities by age group? Please answer it with proper reference.
Thank you in advance.
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I estimate a Cox proportional model in Stata to see what covariates are associated with time to death. One of the covariates measure comorbidities and has 12 categories. One of the categories ("Leukemia") has a ridiculously small hazard ratio and standard error and the upper limit of the confidence intervals is not calculated. In the data, I noticed that people with this specific comorbidity do not experience death but people with any of the 11 comorbidities experience death.
1) Does Cox proportional survival model request an event/category? (e.q. say all males and females must experience the event)?
Any suggestions 2) why I have such a small hazard ratio and no confidence intervals and 3) what to do next?
Many thanks!
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If there are no deaths in those patients who had Leukemia, then the model can't estimate the 'death rate' associated with those patients - hence the unreliable estimates - you should really get a warning / error message from Strata [any time you get a ridiculous CL - your software should really tell you that parameter is unfittable]. In our Statistical Analysis plans, we also write text stating that any sub-group analyses will only be performed if a minimum number of patients belong to any subgroup. For Proportional Hazard models that text is amended to a requirement for a minimum number of events in any subgroup. Hence before fitting your model you should first summarise the number of patients and number of deaths associated with each of your comorbidities. I would suggest any comorbidity that occurs in less than 10% of your total sample size should be excluded from your model, and that ideally you require at least 20 deaths (for a reliable estimate of the death rate) for each of your comorbidities. These numbers are not fixed rules of thumb, if you had a million patients then 10% would be too high - but 20 deaths probably too low. If you had only a 100 patients then I wouldn't even bother looking at subgroups.
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1) In short, do you need to check for multicollinearity when you only have one continuous independent variable (but several dichotomous independent variables)? I have been told by a statistician that i don't. However, how should I handle close association between two binary independent variables in that case?
2) A follow up/more detailed question on difference between adjusting for a covariate and doing stratified analyses
I am using logistic regression to evaluate the association between a binary independent variable of interest ("disease A") and a binary dependent variable ("Condition x"). I have three candidate covariates I wish to adjust for since they have a recognised influence on the dependent variable (age in years, sex and origin). Thus, I have four independent variables 1. "Disease A" (binary) 2. Years (continuos) 3. Sex (binary) and 4. Origin (binary). When I run the logistic regression model, I find a significant association between the IV of interest ("disease A") and the DV ("Condition X").
However, I would like to see if the association between Disease A and condition X is driven by comorbid "disease B".
Here I see two solutions: 1) Two separate analyses on subgroups i) disease A without comorbid disease B and ii) disease A with comorbid disease B or 2) Add disease B as a new independent variable in the logistic regression model.
In subgroup analyses, disease A without comorbid disease B has a small sample size, and type 2 error cannot be ruled out/is likely. Whereby I would like to adjust for "disease B" as an independent variable in the logistic regression model. However, disease A and disease B are highly associated/have a high pairwise correlation. (In VIF diagnostics VIF 2.5 which is the lowest threshold to raise concern about multicollinearity, however, I only have one continuos variable in the model) In the new model, nor disease A or disease B have a significant association with the dependent variable. I believe this is due to the high correlation between disease A and disease B, and that they somehow mask/over-adjust/attenuate each others effect?
I would be very happy to hear your thoughts on this,
Martin
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There's no reason why collinearity can't be problematic with dichotomous IV's.
As an initial step, it makes sense to determine the correlation among your IV's. And also between your DV and each IV. Since your nominal variables are dichotomous, these can just be coded as 0 and 1 and you can use Pearson correlation or Spearman correlation.
In theory, you should be able to include all relevant IV's in the model, perhaps also with interactions between various IV's. However, there are several reasons why this model may not be ideal. One reason is having high degrees of correlation among your IV's. Simple correlation between any two IV's may not reveal this collinearity (which could be among multiple variables together), but it's a start.
From your description, it sounds like you've identified the problem: Disease A and Disease B are correlated. Adding them both in the model results in neither being identified as a significant factor. But one (or either) is identified as a significant factor if included in the model without the others. This is the nature of multiple regression.
I'd say that with the data you have, there's no easy way to disentangle the effects of Disease A and Disease B. You can include one in the model or the other. If they're correlated, there may not be much use to including both in the model. In some ways, having those individual correlations of each IV to the DV help the reader to understand the data and potential implications.
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We are currently investigating an integrated treatment module for patients with PTSD and a comorbid eating disorder. Due to the novelty of the treatment, we wish to asses treatment acceptability (TA).
Sekhon et al., (2017) describe TA as ‘a multifaceted construct that reflects the extent to which people delivering or receiving a healthcare intervention consider it to be appropriate, based on anticipated or experienced cognitive and emotional responses to the intervention’. TA appears to change over time, as various authors state that prospective TA, concurrent TA and restrospective TA may differ. Furthermore, clinicians and patients may differ in their perspectives on TA.
Serveral instruments have been developed, such as Treatment Acceptability/Adherence Scale (TAAS) by Milosevic et al., (2015), which measures prospective TA, or the Distress/Endorsement Validation Scale (DEVS, Devilly, 2004). Previous research has also utilized visual analogue scales or costumer satisfaction reports.
For patient TA, i'm thinking about administering the TAAS or DEVS at different time points (before, during or after therapy) to see how TA changes during the course of treatment. An alternitive idea would be to use a randomisation strategy, where each participant would either receive the questionnaire before, during or after treatment. It would be interesting to also assess therapist TA and to see whether or not these match.
Does this seem like a logical set up? Are there any methodological considerations to take into account? All feedback/suggestions are welcome, thanks in advance.
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Following random assignment, I analysed the differences of categorical variables (gender, education, comorbidity, ADHD subtypes etc) between exp. and cont. groups and the results showed indifference in all demographics. But i failed to analyse indifference of the scale measures at pretest level. After first stage of the intervention, i run t tests and perceived that there was a intergroup difference for the key variable (just one of eight variables). Although I've already started the intervention, can I do something to rule out the baseline difference and to set the internal validity? I thought about to run a suitable covariant analyses but I'm not sure if it works. Do you have any advise for me?
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Hi,
There is no preassumed covariable. Over intergrup difference of ADHD values at baseline, I wonder whether I can see groupXtime effect by controlling this difference. The only way I can think to control it is covariance analysis. Are you suggesting to interpret within group change but not between group differences over time?
Actually, I designed an RCT at the beginning and applied stratified random allocation. Over this difference, my supervisor criticised me that my study failed to meet RCT assumtions. So, I look for any other way to interpret groupXtime effect because I cannot eliminate the difference. Do I understand right, are you agree that the study is not an RCT?
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M group has worked on the gene database of AD, PD, SZ, SZ and ALS. I wonder if there is any gene database compiled for hypertension, diabetes, or other comorbidities. Please let me know.
An example of the analysis is available at:
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I find, for example, diabetesgenes.org (https://www.diabetesgenes.org/), but it does not have a list of the genes but more processed information. I would like to learn human genes with meta-analysis or some kind of risk assessment. I would appreciate it.
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I am having a tough time finding raw datasets. Anyone have any leads on this?
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Need to assess the relationship between comorbidity and Quality of Life among a hospitalized patient'group. To measure comorbidity, I am hoping to use Charlson Comorbidity Index, but it is not clear to me how I can do it (Calculation of score) in the proper way. Any proper reference?
Thank you
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Dear Piyumi,
Charlson Comorbidity Index is a system for evaluating life expectancy at ten years, depending on the age at which it is evaluated, and the subject's comorbidities. In addition to age, it consists of 19 items, which have been shown to influence in a specific way the life expectancy of the subject. Initially adapted to assess survival at one year, it was finally adapted in its final form for survival at 10 years.
You can find online calculators on the internet that may be helpful to you. See: https://www.mdcalc.com/charlson-comorbidity-index-cci
References:
- Charlson ME, Pompei P, Ales KL, MacKenzie CR .: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40 (5): 373-383
- Charlson ME, Charlson RE, Paterson JC, et al .: The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients. J Clin Epidemiol 2008; 61 (12): 1234-1240
Good luck and many successes in your work
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SARS COV2 while still burning through the US, we and other nations have researched practically every topic that this Virus could have. Everything from the Spike Protein on the surface binds to our ACE2-R to the Disseminated Intravascular Coagulation, also known as (DIC). One of the research topics that caught my eyes was the Comorbidities leading to serious outcome cases and unfortunately death.
Mississippi Statistical data shows the number one cause for serious complications and is common comorbidity is Hypertension (HTN) and related cardiovascular disease (CVD), (MSDH, 2021). That said, the problem surrounding HTN and CVD is the increased expression of ACE2-R, thus leaving subjects with increased susceptibility to not only more infection but certainly increased shedding of the virus. This overexpression of ACE2-R is a popular subject in many think tanks and schools of thought.
One would surmise that other comorbidities would have similar if not worse increases in ACE2-R in SARS COV2 infection. Asthma and Chronic Obstructive Pulmonary Disease (COPD), in particular, has a risk in that the pulmonary system is compromised pre-infection which you would think would be a good predictor of severe case outcomes and death. Researchers from the Journal of Immunology and Clinical Immunology found that indeed this was not the case. Interestingly, asthma, COPD, inhaled corticosteroid treatment, and oral corticosteroid treatment were not independent risk factors for ICU admission or death, (Calmes, 2021). Please read the Journal Articles below for your enhanced SARS COV2 knowledge and education.
References:
Calmes, D., Graff, S., Maes, N., Frix, A. N., Thys, M., Bonhomme, O., Berg, J., Debruche, M., Gester, F., Henket, M., Paulus, V., Duysinx, B., Heinen, V., Dang, D. N., Paulus, A., Quaedvlieg, V., Vaillant, F., Van Cauwenberge, H., Malaise, M., Gilbert, A., … Schleich, F. (2021). Asthma and COPD Are Not Risk Factors for ICU Stay and Death in Case of SARS-CoV2 Infection. The journal of allergy and clinical immunology. In practice, 9(1), 160–169. https://doi.org/10.1016/j.jaip.2020.09.04
Nawijn, M. C., & Timens, W. (2020). Can ACE2 expression explain SARS-CoV-2 infection of the respiratory epithelia in COVID-19?. Molecular systems biology, 16(7), e9841. https://doi.org/10.15252/msb.20209841
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I agree with your statement. I still would not like to find out personally as i am an asthmatic.
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The media is mostly pointing on the number of new COVID19 cases ,test positivity rate and recovery rate. Now the news is shifting to vaccination. How are those who recovered from covid coping with existing comorbidities. Has their quality of life changed?
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Yes: Of course ... especially the one already known as "Post Covid Syndrome" with serious neurological and muscular disorders, chronic fatigue, etc; In addition, several experts warn that, in the long term, it is quite possible that it generates various Dementias and Nurocognitive Disorders, perhaps due to the neurotrophic nature of the Covid-19 coronavirus, as is happening with HIV / AIDS.
Happy New Year 2021!
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I have run a study on auditory perception with a sample of children with ASD. Given the high rates of comorbid diagnoses in individuals with ASD, I have some participants with multiple diagnoses (e.g., ADHD, anxiety, speech-language impairment). Is there a way I can control for these comorbid diagnoses so I do not have to exclude these participants? Is there a way to use secondary diagnosis as a covariate? Could I run the analysis with and without the participants who have additional diagnoses? Thank you in advance for any feedback.
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You can not exclude the patients with anxiety and speech delay, since these are autistic feautures according to the DSM-V. It's hard to select patients with pure autism, because it always comorbid with other disorder such as ID, Epilepsy, regression, without neglecting the "syndromic autism". So you may just exclude the ADHD patients since it's a disorder that could influence your results and statistics.
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Dear all,
Would anybody have an idea of the expected value of the Charlson Comorbidity Index in 60 years old adults from the general population (not hospitalized or in requiring healthcare).
Thanks !
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Thank you Carey for these ideas!
best regards
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Running a new group for those diagnosed with both ADHD and ASD as adults. How can I evaluate the impact of the group and perhaps compare to the ASD only and ADHD only only groups?
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Your problem is that both ADHD and ASD are extremely broad diagnostic categories, so much so that your study is very likely to become a casualty of their width. I doubt that you would be able to generate both significant and interesting results. Simply, many with either or both diagnoses are extremely different from each other. The fundamental problem is that the diagnoses are too vague, the 'spectrums' too wide.
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If possible, then there will be no requirement of creating billions of doses for vaccine and cold storage infrastructure. Also it can spread at a high rate and there will be no need of manual vaccination to all. I think in Pandemic times our research of vaccine should be in this way. In conventional method lot of time and money required and for poor and developing countries it will be very difficult to fill demand of vaccine to their people. If some one can create artificially a virus like corona then i think its time to create another virus which can act as vaccine and have low pathogenicity high infectivity and high immunogenicity.
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@ Mudassir Khan, You are right. But we can control rate of mutation in an artificial virus (Vaccine). As rates of spontaneous mutation vary amply among viruses. RNA viruses mutate faster than DNA viruses, single-stranded viruses mutate faster than double-strand virus, and genome size appears to correlate negatively with mutation rate. Viral mutation rates are modulated at different levels, including polymerase fidelity, sequence context, template secondary structure, cellular microenvironment, replication mechanisms, proofreading, and access to post-replicative repair.
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In the wave of corona,and amidst lock down, and social distancing, one section of our society who are at greater risk are the older people, people above the age of sixty five. Lower immunity levels and co- morbidities put the geriatric population at greater risk. Is this sect of population are also having greater risk of psychological issues like loneliness and , fear of death leading towards depression?
Inviting discussions and responses..
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Social distancing is affecting mental health in elders as well as youngers.
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Could ARDS in Covid-19 patients result from inferior waste clearance by a compromised lung microvasculature causing local build up of excess cytokines?
I am VERY unimpressed with the magnitude of elevation of cytokine levels via blood tests. If I correctly recall some analyses I conducted on patterns of cytokine elevations during cytokine storms, you would see many multiple units of magnitude deviation from normal values. Covid-19 patient values are far from this, and it seems like labeling these blood levels as storms is a stretch. Thoughts? Am I mistaken?
Further, death attributable to organ damage associated with uncontrolled cytokine storms kill the young with robust immune systems. These storms are generally considered an over-reaction of a healthy immune system. Covid-19 is remarkably sparing of the young. Covid-19 is burning through the old and elderly, particularly men. These guys can barely get a good piss going, no less any kind of big storm.
Interestingly, I DO believe that a sizable contribution to patients developing ARDS is due to extensive damage to lung tissue attributable to a cytokine response. I believe there is a localized cytokine storm in lungs attributable to lack of normal, timely clearance. Creation of tissue damage due to slow clearance, this new lung tissue damage provokes a second cytokine response.....
Reviewing list of comorbidities significantly associated with Covid-19 patient death, with the exception of heart disease, I believe they all share a major common, physiological impact. Diabetes, high blood pressure, ......regularly lead to narrowing of capillaries as well as loss of their flexibility to expand is well documented as present lung tissue, among many places. This may impact effective clearance of over-sized WBCs as body is actively battling an infection, further degrading waste clearance ability by slowing or blocking blood flow.
As for obesity, it is highly coincident with high blood pressure and diabetes, so I suspect it’s association with increased mortality is predominantly mediated through the effects of the other significant comorbidities.
Heart disease is the exception that proves the rule. Great stress is placed on the heart under the unrelenting pressure to increase throughput in response to oxygen starved systems, even while it is also oxygen starved. This is not a sustainable situation in any event, and has an even shorter time horizon with an unhealthy heart.
Ive read that many believe the deficiency in ACE2 by Coronavirus interferes with normal vasculature regulation and that has prompted trials of ACE inhibitors to further drive regulation through a sole mechanism prompting a increased blood flow. Could work if vasculature can be responsive, can’t work if microvasculature is to compromised by effects of comorbid conditions.
Thoughts?
Has this already been discussed by others elsewhere?
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This paper help you out
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Recently, many countries along with Bangladesh, producing Remidesivir in mass scale and using in COVID-19 treatment. But best of my knowledge, the remedy has some contradictions when it comes to older patients with critical heart condition and comorbidities.
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Hi
Please check the following link. I believe it will help you.
Regards
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A 70-years old woman presented cholestatic jaundice (direct bilirrubine = 10,0 ng/dl and total = 12 ng/dl)). She performed a MRCP, where was observed Type IV hilar cholangiocarcinoma =a stop above from biliary junction. She presents a good status performance and no comorbidities. Her albumine is 3,8 ng/dl and she presents no lost of weight. She presents no distant dissemination. CT volumetry have showed a 30% of FLR for as right trisectioniectomy as left trisectioniectomy. There are no vascular encasement either portal vein or hepatic artery. The main questions are: Should we try any type of resection? What kind? Should we drain both lobes by transparietal approach before the surgery? Is there any role for portal embolization ?
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In cases like this one that we don't know perfectly that there is no vascular involvement in the hillus I will first performed a hillus dissection and if there is not vascular involvement I will go ahead and performed a rightrisectionectomy however if there is an unexpected vascular involvement of left vessels I still could performe a left resection If I had performed previously a right portal vein ligation i will be in trouble
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Hi everyone, I am looking for ways to quantify psychiatric comorbidity / comorbidity severity in our sample** (primary diagnosis of interest: major depression) more elaborately, rather than simply reporting the average number of present comorbid psychiatric conditions. Are you familiar with indices/scores I could calculate for each patient, that, for example, allow weighing different conditions differently (I would perhaps naively assume that personality disorders would receive a greater weight with respect to comorbidity severity than, let's say, a specific phobia). Data collection has already been completed, so unfortunately I'm unable to apply additional questionnaires/assessments. (Comorbid) Diagnoses have been established by SKID-interviews and I am hoping to find a way to build on those.
Any ideas are greatly appreciated, thanks so much in advance!
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While your data collection completed, now you need to use suitable software (like SPSS) to help you in running your data in order to get the results.
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Understanding the complexities of various syndromes and the associated behavioral manifestations , what are alternative therapies to manage such patients?
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Actually, there are only two challenges - a good education and excellent, rich experience.
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Most publications that we have come across have analyzed comorbidity as a COUNT rather than a score: one anecdotal case can be found here: http://circinterventions.ahajournals.org/content/10/1/e004472.long where the authors analyzed comobidities as a binary variable (<4 Elixhaser Comorbidities vs >4 Elixhauser Comorbidities). The concern with using this method is that some comorbidities (for eg: immuncompromised status ) have a higher score (in both Elixhauser and Charlson) and even more so in the clinical sense.
We are aware that Charlon comorbidity scores are analyzed by grouping them into 0, 1,2, >2 and some other ways. We couldnt find a validated grouping method for Elixhauser. Any leads will be appreciated.
Regards,
Mohammed Ali Alvi
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:Med Care. 2015 Sep; 53(9): e65–e72. doi: 10.1097/MLR.0b013e318297429c
Why summary comorbidity measures such as the Charlson Comorbidity Index and Elixhauser score work
Steven R. Austin,1 Yu-Ning Wong,2 Robert G. Uzzo,2 J. Robert Beck,2 and Brian L. Egleston
The newly created Elixhauser Comorbidity Index is obtained via the summation of points from each disease and the range of possible scores is from -19 (lesser disease burden) to +89 (greater disease burden).
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I would like to want to know how to obtain the permission to use for one / all of the following Comorbidity Scores?
If you know the contact e-mail or the respective on-line page. Thank you!
Charlson Comorbidity Index (CCI)
Elixhauser Comorbidity Index – Total Score (ETS) / Point System (EPS)
Functional Comorbidity Index (FCI)
Comorbidity Count COUNT
Rheumatic Disease Comorbidity Index (RDCI)
Psoriatic arthritis comorbidity index (PsACI)
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Dear Dr Salom,
I was referring to condition in a person ... Meanwhile, I received the information needed for the last two scores listed.
Best regards, Alina
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During ICD-9 to ICD-10 codes conversion (using ICD-9 to ICD-10 crosswalk provided by CMS), I have found multiple ICD-10 codes for single ICD-9 code. Since there is no way to confirm which ICD-10 code represents best, should I use any one ICD-10 code for a particular ICD-9 code (probably the first one to be consistent)? Or I should drop the observation from my analysis? Please suggest...
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Fair enough if there are not too many.
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With MRI you can see the frequent comorbitity (or etiology !( of migraine and chronic ethmoidal, sphenoidal, frontal and maxillar sinusitis!
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Clinically, sinus headache and migraine are very similar, so it is no surprise to find excess sinus abnormalities on MRI in migraine.
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We know that Charlson- and Elixhauser comorbidity index use 19 and 30 particular conditions respectively to calculate the comorbidity score. Why other conditions are not necessary in this type of index?
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Thank you Enric Aragonès
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Hello,
My objective of my research is to find patient-related factors that can predict my outcome. I am interested if number of comorbidities could predict my outcome. I have two ways to put that variable in the binary mutivariate logistic regression in SPSS:
1) put variable 'number of comorbidities' in multivariate regression and then I set zero comorbidities as reference . The disadvantage of this method is that patients with 2 comorbidities would be seen as different group than patients with only 1 comorbidity. But in fact patients with 2 comorbidities have also one 1 comorbidity.
2) So that's why I thought it was better to make separate variables: minimal 1 comorbidity + minimal 2 comorbidities + minimal 3 comorbidities and then test it univariate and the variables with p<0.10 would be taken along in the multivariate test.
Which method is better? Or maybe do you have other tips?
Thank you in advance!
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...When you build-up your model, you will see your "categorical" dialog box on your right hand of the SPSS' logistic regression builder.
If you don't want to you use the standard "indicator" coding, or you need multiple reference categories for your categorical predictor (other than the basic 0/1=first/last reference category), you may want to consider selecting the contrast (e.g. Helmert contrasts, reapted contrasts, polynomial coding (indicator)...)... Ideally, you may want to keep it as simple as possible and build multiple covariates with dummy coding (0/1) (use the "last" control category editor; or "first" as an alternative coding).
There are many more options and the interpretation of the output may require some attention. You will find a good reference in Chapter n.20 of Prof. A. Field's "Discovering Statistics Using IBM SPSS STATISTICS", Fifth edition, which is an affordable ($35 bucks at Amazon.com), yet user-friendly textbook for most of the basic-intermediate procedures. Hope this will hep. Michele.
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I am estimating the cost of HTN. the comorbidities cost account for a major burden on people's pocket. Can I consider and report these cost of comorbidities as incremental cost to the original cost of treatment (HTN)?
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In my opinion it must be an internal environment cost
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I am looking for papers (or other resources) addressing the treatment of comorbid (non-veteran) posttraumatic stress disorder and schizophrenia. I haven't had any difficulty finding materials on epidemiology or differential diagnosis, but I keep on hitting a brick wall when it comes to studies on treatment.
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The data set should include patient demographics, symptoms, family medical history, comorbidities, certain lab test results etc.
Thanks
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In most of the studies, especially in cancer patients those are with other comorbidities are always being excluded.
I want to know the reason why they are excluded, and please anyone suggest me, if we want to include them what are the necessary steps that are required to be taken before starting the research. because I'm interested to work on this type of population.
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It depends what is the goal of your study. If you want to emphasize more on external validity (generalization), inclusion criteria can be broader. However, you do loose quality in internal validity. The goal here could be to test the effectiveness of a treatment (already supported in RCTs) in clinical/community setting.
If the goal is to validate the efficacy of a treatment, you must rule out as many moderators as you can and therefore, you must exclude participants who do not meet the exact criteria. This improves your internal validity but is hardly generalizable to real-world practice
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Hello everyone.
I can't figure out how to solve the problem. I think, the task is not difficult, but it has me stumped.
I have a comorbidity scale scores at the initial stage (S1) and before the surgery (S2). Period before surgery (T) in all individuals is different. I need to evaluate the linear link between T and S2 with adjustment for S1.
I found the following solution of the problem:
- partial correlation (can I use if two of the three values are paired observations – S1 and S2);
- use a linear regression model and postestimation with fixed value of S1 (the same problem);
- use a regression model with random effects to overcome the constraint on independence and normality of residue distribution;
- to use pair correlation of delta (S2-S1) and T. As me seems, the most simple, but not the most the right method.
I use Stata and SPSS. I want to solve this problem in these packages. How to do it right?
Thank you.
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The following example uses Stata's xtmixed:
clear
set obs 1000
set seed 2805
gen id = _n // patient id
gen t = rpoisson(10) // period
gen s1 = rnormal(100,10) + 1.5*t // correlated t and s1
gen s2 = 1.2 * s1 + rnormal(0,10) // correlated s1 and s2 (and t)
pwcorr(s1 s2 t)
// make data long format
expand 2
gen s = s1
forvalues i=1(1)1000 {
local target = 1000 + `i'
replace s = s2[`i'] in `target'
}
gen measurement = 0
replace measurement = 1 if (_n > 1000)
order id measurement s s1 s2 t
sort id measurement
xtmixed s i.measurement t || id:,mle cov(unstructured)
In case that you want a standardized effect size, you may consult the following example:
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This is my first ever medical statistics/epidemiology questions, so please be patient if I come across as naive, I normally focus on drug and protein chemistry.
I have a huge medical dataset. From this set I have divided up the population by certain characteristics, a particular disease, an age group, and gender. I have four disease to consider over seven age groups of gender, thus 56 sub populations. Per sub-population I determine the prevalence within that population of reported comorbidities. This will leave me with 56 lists of "disease (a name) - prevalence ((0-1])". That is a lot of data. Putting aside a particular hypothesis or particular descriptive question, how would you go about displaying this amount of data in a report or a publication?
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I agree with Michael S. Martin.
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How high AGEs intake is linked to PCOS
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follow this link may be useful for your project
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A young male 30 years old healthy BMI 29 ; no comorbid condition presents with superior mesenteric ischemia- what is left is T-colon and Duodenal stump;
On laparotomy; tube duodenostomy is being done; how to manage such a case ??
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Dear Ahmad.
You can get information on AMI here 
It is unclear which type of AMI you have met in that patient. Please, pay attention to NOMI and necessity of pharmacological correction. In any case, now you have to manage full duodenal external fistula and a short bowel syndrome. If so, then: 1. TPN and 2. anastomize asap. 
Sincerely Vladimir M.Khokha.
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I have three biomarkers that are used to predict postoperative death. One of the reviewers commented that I should adjust biomarkers with comorbidity CIRS G score. I used to try hierarchical binary logistic regression but I am not sure that this is the right way.
Is there a way that I can adjust biomarker values for every patient individually with CIRS score (continuus) through Compute variable option? And which sintax would it be?
Thank you in advance.
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ok Danica, these AUCs look pretty high and good. But you should also look at the estimates and p-values of your biomarkers and the CIRS score in the logistic regression. Are both significant? Most importantly, is the coefficient of the biomarker still significant?  If this holds true, then you can claim a prognostic role of the biomarker "after adjustment for the CIRS score".
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I need to adjust my biomarker results with comorbidities, age and sex. Hopefully I will be able to enter the results into the ROC curve and compare it to original ROC curve (without adjustment).
I have been wondering if u could use linear regression (with age, sex and/or comorbidities being dependent and biomarker being an independent) with adjusted predicted value checked and then insert the obtained adjusted value into ROC curve as a test value? Is this the right method?
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Well, I did the ROC curves based on binary logistics (biomarker specificiti and sensitivity for postoperative mortality). But then one of the reviewers asked me to adjust the results for CIRS G score (comorbidities). I was wondering what would be the right way to adjust my results with the score he requested. Thank you.
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Somebody have experience using comorbidities index such as Elixhauser index or Charlson index in burns patients.
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I have used Burns Depth Index but not exactly comorbidity index.
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Are the comorbidities an important impact factor ? And if yes, a correct therapy of comorbidities can reduce and/or remove the impact of vitamin D  on cognitive performance ? 
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EHRA scores are used to discriminate symptoms severity when patients feel they are in AF. However, patients still complain of disabling symptoms when they are in constant sinus rhythm post AF ablation. What is the validity of using EHRA score post procedural when the patient's general condition due to other comorbidities is restricted (arthritis, COPD, asthma, etc) while they are in sinus rhythm all the time?
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I am not an arrhythmologist (I am a general cardiologist), but in my opinion, the EHRA score can be used for assessement AF related symptoms and to queality of life after ablation. therefore, maybe pre and post ablation EHRA score is useful for detection of effectiveness of the procedure. 
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i am interested to find research (publications or draft) on the use of mobile applications and peer education in the field of MOBILE APPLICATIONS (or web development) in the area of HIV of Ageing or Comorbidities.
Purpose is to find if 'lifestyle' behaviour changes can be changed with education in HIV 50+ patient population.
Axel Vanderperre
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I have done some search on the use of apps in HIV to find out there are too many in the market, they compete for attention and they tend to be devoted to one aspect (e.g., treatment, lifestyle, socializing). I can share this search at director-EDUC@ohtn.on.ca 
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We all know that the velocity of a point in the proper distance
increases since it keeps changing, what about velocity of a point in the comoving distance?
Lets assume we are observing a galaxy at point A and we record it's velocity, after sometime it moves on and that same point is replaced by galaxy B. Will there be any differences in velocity between galaxy A and B basing on the current model of the Universe?
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ME: Lets assume we are observing a galaxy at point A
ME: Will there be any differences in velocity between galaxy A and B
What is "A"? Is it a location, described by a distance from us which doesn't change, or is it the label of a galaxy which gets farther away? You need to be more careful in how you ask your questions.
ME: Lets assume we are observing a galaxy at point A and we record it's velocity, after sometime it moves on and that same point is replaced by galaxy B. Will there be any differences in velocity between galaxy A and B basing on the current model of the Universe?
Let's ignore local peculiar motion and consider comoving galaxies so galaxy A is at a fixed comoving distance and galaxy B is at a fixed but smaller comoving distance, and we can define a location X as being at some fixed proper distance D.
At some time t1, galaxy A is at location X receding at proper speed VA1. At some later time t2, galaxy B passes location X receding at speed VB2.
The Hubble law says the speed is proportional to the distance D but the coefficient is falling with time so VB2 < VA1.
If these terms aren't clear, I would recommend reading the Lineweaver paper which clears up a lot of confusion.
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Running a Syst Rev, finished databases and contact with main authors, just looking for any new datasets not published/thesis?
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Dear Bruno,
I am writing a manuscript about ADHD and eating disorder in primary students. Is that interest you? If yes, you can contact me by email. ltong@fudan.edu.cn
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Which drug can be used as monotherapy in BPH/LUTS . Tadalafil vs Tamsulosin.
Tadalafil was shown to be significantly effective for improving LUTS/BPH. Significant improvements in IPSS and the IIEF score were also observed in patients with comorbid BPH and ED.
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Well in my 40years of work in urology i had the impression that the efficiency of my medication was always so good as the patient believed in it. That' s why i remained in uro-surgery Your Hainz
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Scales that can be used in detecting comorbid depression and anxiety in the elderly?
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Thanks a lot every one.what do you think about GDS 15 and GAD 7 these scales have been validated in my study location as comoared to others mentioned. At Ronan I'm interested in some types of anxiety especially the types common with the elderly. Sorry for the late response.
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When reading articles using the Charlson Comorbidity Score (Charlson ME et al J Chron Dis. 1987;40:373–383) to describe the comorbidity of a cohort of patients with cancer, for instance head neck cancer, I have the impression that some authors include the primary tumor into the calculation (Charlson's category "any tumor" with 2 points) and others not (only when the patients have at the same time or within last 5 years another type of cancer). The same problem I see with Charlson's category "solid metastatic tumor = 6 points). Some authors seem to include the primary tumor if metastatic (M+) into the calculation, others not.
What is correct?
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Lieber Orlando,
da es ein Comorbidity Score ist, sollte die Grunderkrankung nicht hinzugerechnet werden.
Liebe Grüße und ein Frohes Neues Jahr
Christoph
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TKIs made significant improvement in treatment of GIST. Underutilization of TKIs in adjuvant treatments might be the consequence of a) patient's charactersitics as several chronic comorbidities at advance age, b) problems with level of health insurance, c) level of income in a country; or others. 
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It is generally accepted that GIST arises from interstitial cell of Cajal with active mutation of c-KIT, also referred to as CD117. Some of the cases also harbor the active mutation of PDGF receptor-alpha, which is also inhibited by TKI characterized by imatinib as well as mutant c-KIT. However, as you imply, there is some population that neither c-KIT nor PDGF receptor -alpha harbors genetic mutation.  Further, cases with point mutation in exon 9 in c-KIT tend to exhibit resistant response to imatinib treatment. Given the heterogeneous biological characteristics of GIST genetics, your clinical trial would be great important!!!   
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Can anyone recommend literature on hepatits C and depression/psychiatric disorders as hcv-induced comorbidity?
regards!
chaim
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Thank you very much!
Chaim
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The "Physician's Diseases Related Groups (DRG)" is a publication of an insurance created association of all diseases. Created by insurance actuaries for use in managing health care expenses, it is a statistical relation of diseases / conditions that physicians use to diagnose / treat. Diagnosis is not the same as statistical groupings, but the economics of medicine demands it. Medicine defines treatments by category, but often a diagnosis is vague. A patient may present with episodes of violent rage. These occur in the brain. Naming it as a psychological problem or a neurological condition may be difficult (perhaps it should be called 'whatever'). A patient might be referred to either a neurologist or perhaps a psychiatrist for care. The patient needs to be treated by both disciplines. Insurance coding lists of these bodies of evidence based diagnostic diseases databases. I am researching better patient care by adding a modifier in diagnostic coding. Better patient care will improve the patient's health, and cut insurance costs by avoiding unwarranted care. I need both data on diagnostic groupings used today as well as physician input of how they might prefer to see patient diagnostics coding. I am looking for a computer database that shows the co-morbidity of diseases, and is not limited by showing insurance codes alone
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Do you mean data collections such as inpatient and outpatient hospital admissions/separation datasets (which use ICD codes for the primary and other diagnoses.  Perhaps Medicare databases?
I am not sure if this is what your question was getting at, but I hope it helps..
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Co-morbidity is any other illness apart from indexed disease
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Co-morbidity can allow you to stratify the risk of both morbidity and mortality associated with surgery. Scoring systems as Possum etc combined with tests such as CPET will allow you to counsel patients as to risk and stratify them according to level of post-op care they will need (Itu) or if you should operate at all.
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Apart from the effects of dietary changes and variable therapeutic compliance,do you know if there are other sources of comorbidity (apart of diabetes, thyroid status and cholestasis) that may cause significant and temporary variation in the concentrations of major lipoproteins (without the pharmacological treatment) ?.
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Bile malabsorption can also increase serum triglyceride levels, while decreasing the cholesterol pool. This might be an autoimmune phenomenon, but I'd love to hear anyone's thoughts on the matter. And yes, these changes may be transient.
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The current trend is towards resection of suspected Stage 1(cT1N0M0 on PET) lung cancer when biopsies (CTFNA) are inconclusive, given the limitations of sampling. In case the decision is taken for interval imaging rather than resection (eg by VATS), are there any features of the nodule that would favour such an approach? I would presume lower SUV and slow growth as the most obvious. The situation usually arises in patients with WHO PS2 with comorbidities, who may be suitable for radical treatment at Stage 1 but not at Stage 2 or beyond. The concern of course is that the interval scan may show metastatic or incurable disease and you don't want the patient to "miss the boat". Remember also "Primum non nocere". 
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Dear Lawrence, this is an interesting question for which there are some (but not all)  answers.
As you indicate there is a balance of risk between "primum non nocere" and early resection resulting in cure of a malignant process. 
I would refer you to the attached papers
a. Fleischner Society recommendations for the management of a solid nodule - Radiology (2013)
b. Furman et al (2013) Future Oncol. 2013;9(6):855-865.
c.  American Association for Thoracic Surgery guidelines for lung cancer screening
In addition to growth rate and SUV on PET, which you mention, there are other the factors:
1. Those which increase pre- test probability, namely smoking burden, current and remote history of malignancy,  family history, exposure to radon gas, asbestos and certain metals (e.g., chromium, cadmium and arsenic), pulmonary fibrosis, HIV infection, asbestosis, haemoptysis and constitutional symptoms.
2. Shape (Spiculation)
3. Contrast enhancement
4. Location - upper lobe increases risk
5. Ground glass opacity
6. Absence of a polygonal shape, Thickness of a cavity wall > 15mm
There are separate guidelines for ground glass opacities
Hope this helps
Best wishes
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Hiya - I am looking for a research instiute or dept, or even just some studies which look at work and return to work when experiencing mental and physical health comorbid health condtions. Any help gratefully recieved.
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I would review work by Dr Geoffrey Waghorn below is a small sample of his publications, he works at Queensland Centre for Mental Health Research,
Waghorn, G., Saha, S., Harvey, C., McGrath, J. et al. (2012) Earning and learning in people with psychotic disorders. Results from Australia’s second survey of psychotic disorders. Australia and New Zealand Journal of Psychiatry, 46(8), 774-785.
Waghorn, G. (2012). Implementing evidence-based practices in supported employment for people with psychiatric disabilities. Editorial for Special Issue. Journal of Rehabilitation, 78(1), 2-3.
Waghorn, G. Childs, S., Hampton, E., Gladman, B, Greaves, A, Bowman, D. (2012). Enhancing community mental health services through formal partnerships with supported employment services. American Journal of Psychiatric Rehabilitation, 15(2), 157-180
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I need to know some activities and exercises for poor and illiterate elderly having more than 2 diseases, and living in communities.
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The answers will be things individuals prefer to do, daily type activities, have experience doing, historic review, have in common, interactive, cognitive, lightly challenging, fun or would like to learn. Free lectures and community events. This website I just encountered might give you some ideas. http://www.caring.com/articles/activities-for-dementia-alzheimers-patients.
A search in "low cost ideas for activities with older adults" yields many ideas.
Perhaps there are community members who wish to assist and adopt a program to help out with engaging this population.
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I see both terms used widely when reviewing funding proposals and would like to know if there is a difference.
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Dear Philip
Comorbidities : A concomitant but unrelated pathologic or disease process; usually used in epidemiology to indicate the coexistence of two or more disease processes at the time of diagnosis.
Multimorbidities: can be described as existence of two or more chronic diseases before of diagnosis
In the health care system, comorbidity and multimorbidity carries considerable weight in determining the reasonable length of hospitalization
Reference:
Concepts of comorbidities, multiple morbidities, complications, and their clinical epidemiologic analogs : Anne Gulbech Ording and Henrik Toft Sørensen
Clinical Epidemiology 2013
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I will use SCID II interview for borderline disorder (just borderline section), however, I need to have one or two questionnaires to give me an account of the comorbid axis I and II disorders as well.
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You already posted this question in oktober, are you still in need of suggestions? I agree with Aliza Krieger who suggested the SCID I. You will only use a specific section of the SCID II, why not use the whole interview? Another option for axis I disorders is the MINI (see below for reference) which should take less time compared to other options like the SCID.
good luck!
Sheehan, D.V., Lecrubier, Y., Sheehan, K.H., e.a. (1997). The validity of
the Mini International Neuropsychiatric Interview (M.I.N.I.)
according to the scid-p and its reliability. European Psychiatry,
12, 232-241.
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Comorbid anxiety disorder is common in patients with an ASD. I'm looking for papers which have trialed medications for anxiety in the ASD population.
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You might find something in this paper