Non-Adherence, Whether Intentional or Not

Non-Adherence, Whether Intentional or Not Often times patients being prescribed medication that will discontinue use, more than likely making this decision themselves and without informing their health care provider or health professional. As a result, there are major costs and even the possibility of death associated with its discontinuation. The most common is partial non- adherence. This partial non-adherence is much more prevalent than that of full discontinuation in which it to can also be detrimental. More frequently, patients are adjusting their medication regimen without properly being informed. There are many reasons for failure to comply with medical advice and some of them we will be discussing throughout this paper. We will examine the data from those patients who take their regime as prescribed, those who do not take their prescribed regime and those who don’t do not have a proposed regular regime. The extent to which an individual follows medical advice is a major concern in every medical specialty (Osterberg & Blaschke, 2005). There has been much awareness put forth in an attempt to encourage patients to follow the doctor’s recommendations as prescribed. In addition, the lack of influence in the communication between patients and healthcare professionals is also overwhelming. Particularly, if no clear agreement is formed with the patient at the beginning of the treatment process, then surely in the end should be of no consequence. A doctor has a duty to explain, in terms understandable to the patient, what he or she intends to do before a patient begins a course of treatment. To adhere with the ideology of informed permission, physicians must equip patients with the necessary information for them to make a educated and informed decision. This includes the intended benefits of, alternatives to, and the possible risks and complications of that treatment or procedure. Recent studies, have shown that untimely discontinuation was found to be less than half in patients who recalled being told to take the medication for at least 6 months compared with those not given this information (Bull et al, 2002b). It has proven even more difficult when patients lack insight into their condition. A lesser-known coequal to the doctrine of informed consent is informed refusal. Informed refusal cases occurs when patients claim that they were not made aware that refusing the proposed medical or surgical treatment or consultation would be a risky idea. Patients argue that their physician did not inform them of the potential harm that could result from not undergoing the recommended treatment or consultation; and, that if their physician had appropriately informed them of the consequence of refusal, they would have discerened that the benefits outweighed those risks and would have consented to having the recommended treatment/procedure. Patient adherence to a medication regimen is central to good patient outcomes. In addition, adherence is the quality of the provider/patient relationship. Effective provider/patient communication is empirically linked to positive outcomes of care including patient satisfaction, health status, recall of information, and adherence . Provider discussions help patients understand their illness and weigh the risks and benefits of treatment. Healthcare providers are an essential part of the five interacting dimensions of medication adherence identified by the World Health Organization (WHO) (See Figure 1 and Table 1), which include social/economic factors, medical condition-related factors, therapy-related factors, and patient behaviors. Identifying strategies for improving medication adherence are the responsibility of all involved, but the focus of this Time Tool is on the provider’s role in medication adherence. Figure 1. Five Interacting Dimensions of Adherence Appendix A Table 1. Factors Reported to Affect Adherence Appendix B Poor adherence to prescribed medication is associated with reduced treatment benefits and can obscure the clinician’s assessment of therapeutic effectiveness. Non-Adherence is thought to account for 30% to 50% of treatment failures . Non-Adherence leads to worse medical treatment outcomes; higher, avoidable hospitalization rates; institutionalization for the frail elderly; and increased healthcare costs . Attention to adherence is especially important in the current economic climate where we are seeing an uptick in patients foregoing medications by not filling or refilling prescriptions and hoarding medications due to high costs. Considering all of the factors listed in Table 1 that contribute to poor adherence, on the surface, it would appear that the provider role is very small. Yet this is not the case. Physicians play an essential role in medication adherence. Patients who trust their physicians have better two-way communication with their physician. Trust and communication are two elements critical in adherence advantages. Numerous studies show that physician trust is more important than treatment satisfaction in predicting adherence to prescribed therapy and overall satisfaction with care. Physician trust correlates positively with acceptance of new medications, intention to follow physician instructions, perceived effectiveness of care, and improvements in self-reported health status. A recent meta-analysis of physician communication and patient adherence to treatment found that there is a 19% higher risk of no adherence among patients whose physician communicates poorly than among patients whose physician communicates well . Statistically, the odds of patient adherence are 2.26 times higher if a physician communicates well. This translates into more than 183 million medical visits that need not take place if strong interpersonal physician/patient communication occurs. Communication contributes to a patient’s understanding of illness and the risks and benefits of treatment. Hence, the major challenge is to improve: • Verbal and nonverbal communication (patient-centered care) • Interviewing skills (improved competency) • Discussions and provide greater transmission of information (task-oriented behavior) • Continuous expressions of empathy and concern (psychosocial behavior) • Partnerships and participatory decision-making (patient-centered care) Poor adherence to medical treatment is widespread and well recognized, as are its consequences of poor health outcomes and increased healthcare costs (See Figure 2) . Non-Adherence to medications is estimated to cause 125,000 deaths annually. Consider these other statistics: • Overall, about 20% to 50% of patients are non-adherent to medical therapy • People with chronic conditions only take about half of their prescribed medicine • Adherence to treatment regimens for high blood pressures is estimated to be between 50 and 70 percent • 1 in 5 patients started on warfarin therapy for atrial fibrillation discontinue therapy within 1 year o Underuse of anticoagulant therapy for prevention of thromboembolism is attributed to the risk factors of younger age, male gender, low overall stroke risk, poor cognitive function, homelessness, higher educational attainment, employment and reluctant receptivity of medical information • Rates of adherence have not changed much in the last 3 decades, despite WHO and Institute of Medicine (IOM) improvement goals • Overall satisfaction of care is not typically a determining factor in medication adherence • Adherence drops when there are long waiting times at clinics or long time lapses between appointments • Patients with psychiatric disabilities are less likely to be compliant • Non-Adherence results in an economic burden of $100 to $300 billion per year.  Annually, Non-Adherence costs $2,000 per patient in physician visits  The rate of Non-Adherence is expected to increase as the burden of chronic disease increases • Non-Adherence accounts for 10% to 25% of hospital and nursing home admissions (Figure 3). Recent research has found medication Non-Adherence to result in:  5.4 times increased risk of hospitalization, re-hospitalization, or premature death for patients with high blood pressure  2.5 times increased risk of hospitalization for patients with diabetes  More than 40 percent of nursing home admissions Cross tabulation of reasons for not taking medications as prescribed and the rate of taking medications as prescribed . Appendix C If No, Why? Did you take your drugs as prescribed? Total No Yes No response 2 48 50 1.30% 75.7% 77.00% Cost of medications 6 0 6 6 0 6 3.90% 0.00% 3.90% The nature/busy schedule of work 4 0 4 2.60% 0.00% 2.60% Don't like taking medications 4 0 4 2.60% 0.00% 2.60% Too much medications 1 0 1 0.70% 0.00% 0.70% Side effects(When I feel worse) 10 0 10 6.60% 0.00% 6.60% Forgetfulness 2 0 2 1.30% 0.00% 1.30% When I feel better 8 0 8 5.30% 0.00% 5.30% Total 37 48 50 24.30% 75.70% 100% Majority of the respondents 50 (75.7%) take their medications as prescribed, while 37 (24.3%) do not. In Table 6, the reasons for not taking their medications as prescribed were attributed to cost of the medications 6(3.9%), the nature and busy work schedules 4(2.6%), dislikes for medications 4(2.6%), too much medications 1(0.7%), side effects of medications-when feeling worse 10(6.6%), forgetfulness 2 (1.3%), when feeling better 8(5.3%). However, discontinuing medications when feeling worse, and well were the most common causes of medication non- adherence. Studies revealed some of these factors: cost of the medications. Cost is a crucial issue in patient’s adherence especially for patients with chronic diseases as the treatment period could be life-long. A number of studies found that patients who had no insurance cover, were more likely to be non-adherent to treatment. Side effects of the medications feeling worse, feeling better. Patient’s knowledge about their disease and treatment is not always adequate. Some patients lack understanding of the role their therapies play in the treatment others lack knowledge about the disease and consequences of poor adherence from the study. The statistical (chi square) analysis showed a significant association with P < 0.01. In conclusions, interventions that will address these problems of non-adherence are imperative in order to improve adherence the more. Some of alternative interventions include the healthcare providers improving on the areas of patient education and counseling, communication between them and patients, medication selection with cost consideration and intolerable side effects of the medications, shorter wait time and accessibility of the clinics to the patients. References Smith DH, Kramer JM, Perrin N, et al. A randomized trial of direct-to-patient communication to enhance adherence to beta-blocker therapy following myocardial infarction. Arch Intern Med. 2008; 168(5): 477-483. BENNETT, BRIGGS, TRIOLA (2013). Statistical Reasoning for Everyday Life. ,, 1-77. Osterberg, L. & Blaschke, T. (2005) Adherence to medication. New England Journal of Medicine, 353, 487–497. Mitchell, A. J., & Selmes, T. (2007). Why don't Patients take their medicine? Reasons and Solutions in Psychiatry. Advances in Psychiatric Treatment, 13:336-346. Retrieved from http://apt.rcpsych.org/content/13/5/336.full.pdf+html Bull, S. A., Hu, X. H., Hunkeler, E. M., et al (2002a) Discontinuation of use and switching of antidepressants: influence of patient–physician communication. JAMA, 288, 1403–1409. Bultman, D. C. & Svarstad, B. L. (2000) Effects of physician communication style on client medication beliefs and adherence with antidepressant treatment. Patient Education and Counseling, 40, 173–185. DiMatteo MR. Variation in patients’ adherence to medical recommendations. Medical Care. 2004; 42(3); 200-209.

Why Going to Church Can Make You Fat

Main Task: Analyze Statistics in the News In your activity resources above are three news articles that report on scientific studies and make recommendations on the basis of them. Write a paper analyzing these articles. For each article answer these questions and give reasons for your answers: Why Going to Church Can Make You Fat 1. What evidence does the article provide for an association (correlation) between the phenomena discussed? According to the data provided from the Coronary Artery Risk Development in Youth Adults study provided by the researchers at Northwestern University, people who went to church at least once a week were more than twice as likely as people with no religious involvement to become obese. In addition, previous and or past research noted a correlation between religiosity and weight gain. 2. Drawing on your text’s discussion of how to interpret correlations, what would you want to check to be confident that there is actually a correlation? We would want to know what causes weight gain, what population was sampled, what was the average age group being studied, what religious entities were analyzed, what situations may have taken place that provided a situation to intake unhealthy foods, what other cultural practices occurred, what other practices lead to (caused) increased weight gain, what other kind of social events may have taken place where food was being served? 3. What is the argument that the relationship is causal? The most common kind of evaluation everyone encounters is testing of a causal model. This is the measure property of an individual. Causal models are typically evaluated, initially, with data that describe an association or correlation between variables. The mere suggestion, that religious activities somehow promote weight gain is in itself a casual implication without any further substantiating data being provided. 4. Is the argument for the causal relationship convincing? In my opinion, in this particular case it is quite convincing. Previous research has been done that concurred with the ideology that those who attended church likely had more interactions within social networks, therefore providing more opportunities to entertain with food. 5. If the argument is not convincing, what additional evidence is needed to make a convincing argument that the relationship is causal? Although, the study of a smaller sampling will be easier to work with, it cannot possibly provide or represent the entire population exactly. We would want to know the sample proportions relative to a close or normal distribution, and its mean that would be closer to the population proportion. 6. Does it make sense to make changes in your life based on the article? In my particular situation, it would make sense to change in my life, because when you know better you do better. Also, the information being provided offers informed information and thus causing awareness. Why Having Kids Is Bad for Your Health 1. What evidence does the article provide for an association (correlation) between the phenomena discussed? The evidence provided in this study does offer some evidence of the correlation between moms and their counterparts as it relates to health issues. The researchers focused on some sample that we can look at with hope that it will be representative of the population. 2. Drawing on your text’s discussion of how to interpret correlations, what would you want to check to be confident that there is actually a correlation? More concisely, we would want to check the margin of error for the sample mean, population mean or population standard deviation. Without it we cannot know the true range in which 95% of all sample means would lie. Also, we should ask ourselves does this statement make sense? 3. What is the argument that the relationship is causal? Lead researcher in this study said that “All parents can relate to the idea of demands and trade-offs”. Rochman, B. (2011, April 11) although however, I’m not certain that the aforementioned statement that “All” parents can relate is in fact accurate or not, I do agree somewhat with the notion that parents do have a desire to do better but for other reasons stated cannot prepare healthier meals. 4. Is the argument for the causal relationship convincing? This particular example is convincing enough, in that I can identify with what is being said. Even though, there are certainly other variables that could also yield the same results having children can definitely be a contributing factor. Notably, the time that once was allocated for exercise and other healthy activities is now being pushed aside to deal with the daily demands of parenting. 7. If the argument is not convincing, what additional evidence is needed to make a convincing argument that the relationship is causal? A clear visual of the interpretation of this idea would make this argument more convincing. The use of the 95% confidence interval can be very useful with proper interpretation. Also, determining what other activities the parentless subject were participating in that the parents were not would also help to identify other probable variables. 5. Does it make sense to make changes in your life based on the article? Changing your life after reading this article may not be an issue if you have already been affected by this scenario. Personally, I can identify and or can relate to this claim therefore, it would make sense to just make the best of the situation that is already at hand. On other hand if this has not already happened to you that you can in fact be proactive and informed and attempt to make better decisions. The Link Between Sleep and Weight 1. What evidence does the article provide for an association (correlation) between the phenomena discussed? A recent study following a group of 40-60 year old women for five to seven years whilst tracking their weight and sleeping patterns found that women who reported having trouble falling asleep, waking up frequently at night , or having trouble staying asleep were significantly more likely to have “major weight gain”. 2. Drawing on your text’s discussion of how to interpret correlations, what would you want to check to be confident that there is actually a correlation? You would want to know if there were other underlying issues that also contributed to the weight gain and or the lack of sleep. Therefore the sampling distributions would be useful information when making these types of claims. 3. What is the argument that the relationship is causal? According to Kakar sleep is associated with body weight for two reasons. 1. First, people who are sleep-deprived may have less energy throughout the day and therefore less motivation to exercise regularly. 2. In fact, people who don’t sleep enough report getting less excessive than people who get enough sleep every night. 4. Is the argument for the causal relationship convincing? Yes, this argument is convincing because other research and studies have implicated that when the body sleeps or is at rest that it heals and process foods more efficiently. Therefore, it is a high probability that this claim is in fact valid to some degree. 5. If the argument is not convincing, what additional evidence is needed to make a convincing argument that the relationship is causal? The additional evidence needed to make argument more convincing would be to show how in other cases sleep has lead to other ailments. In addition, provide data showing where those who got more sleep had better BMI than their counterparts. 6. Does it make sense to make changes in your life based on the article? Yes, in this instance like the others it is always best to make better decisions and choices you have been informed and enlighten about particular subject matters. In conclusion, cause and correlation are often misinterpreted or misconstrued. Correlation is a relationship between two or more subjects: when one increases, the other increases, or when one increases, the other decreases. On the other hand cause is something that is the result. The most significant point is that a correlation between two things does not definitely mean that one causes the other. If there is a relationship between two phenomena, A and B, it could be that A causes B, or it could be that B is responsible for A; other possibilities are that some other element is the reason or cause for both A and B, or that they have independent causes that are the same. References BENNETT, BRIGGS, TRIOLA (2013). Statistical Reasoning for Everyday Life. ,, 1-77. McCoy, Krisha, MS (2010). Studies show that skimping on sleep may lead to weight gain. But can getting shut-eye help you lose weight?. The Link Between Sleep and Weight, , 1. Retrieved from http://w ww.everydayhealth.com/sleep/101/tips/snooze-control-suggested-for-overweight-children.aspx Park, Alice (2011). Why Going to Church Can Make You Fat. Behavior, , . Retrieved from http://healthland.time.com/2011/03/24/why-go ing-to-church-can-make-you-fat/ Rockman, Bonnie (2011). Why Having Kids Is Bad for Your Health. Parenting, , . Retrieved from http://healthland.time.com/2011/0 4/11/is-parenthood-bad-for-your-health/?iid=WBeditorspicks Shaoxu, S., & Lei, C. (n.d). Editorial: Efficient discovery of similarity constraints for matching dependencies. Data & Knowledge Engineering, 87146-166. doi:10.1016/j.datak.2013.06.003 Trevor, C., Dominic, W., Roger W., S., Peter, D., & Thomas C., R. (n.d). Discovering discovery patterns with predication-based Semantic Indexing. Journal Of Biomedical Informatics, 451049-1065. doi:10.1016/j.jbi.2012.07.003 Tomasallo, C. D., Hanrahan, L. P., Tandias, A., Chang, T. S., Cowan, K. J., & Guilbert, T. W. (2014). Estimating Wisconsin Asthma Prevalence Using Clinical Electronic Health Records and Public Health Data. American Journal Of Public Health, 104(1), e65-e73. doi:10.2105/AJPH.2013.301396 Auto Detailing Supplies Inc.

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