QUESTION
Population vs Public Health
Part 1 - 200 words
Raise a question from your response – 100 words
CAM528 Introduction to Epidemiology
Module 3-Alternative Explanations
For this task please read the following paper Travel as a Risk Factor for Venous Thromboembolic Disease.pdf. https://mylo.utas.edu.au/content/enforced/392647-AW_CMX_20S2_24095_0_1_0_1_1/Modules/Travel%20as%20a%20Risk%20Factor%20for%20Venous%20Thromboembolic%20Disease.pdf?_&d2lSessionVal=haWCG7BnvirToaltyGJSEV77c
Specifically address on the discussion board the measurement of exposure and outcome in relation to the potential for measurement error related to:
1. Selection bias and information bias, and
2. Have issues of confounding been adequately addressed either in the study design, data collection and/or analysis.
Activities form the basis of your Discussion Board postings and assessment for this module. Please check the Unit Outline for details.
Part 2 - 200 words
Raise a question from your response – 100 words
After your reading, please answer the following questions below.
1. Reflecting on the readings and activities in this module, what are some of the key insights you gained about the application of systems thinking in public health to date?
2. How might this influence your systems thinking practice in public health moving forward?
Defining Public Health
Population vs Public Health
The health system is any system that has a purpose related to health, which includes health care, population and public health.
A common definition of Public Health is that it aims to promote and improve the health of all people and to prevent injury, disability, disease and premature death. Public health is often described as being a science as well as an art. Often public health is used to describe the health of the public or health services and the healthcare system, other times it is used to describe knowledge and techniques.
Importantly public health looks beyond individuals. It is not concerned with individual behaviour change, rather it seeks to develop conditions to shift groups, communities and populations of people in the direction of good health.
Public health is concerned with the health of groups, communities and populations. Sometimes, the term is confused with population health. Public health uses population data to understand patterns health, ill-health, disease, disability and health-related behaviours, as well as the patterns of determinants. It applies population health approaches to improve health outcomes. But public health goes further to consider what “we as a society do collectively to assure the conditions in which people can be healthy.”
There is likely to always be a debate about our choice of terminology but what we seem to be able to agree on is that public health involves concerted action to improve the health of the population as a whole, that our work is in the general public’s interest and that there needs to be a focus on the broader determinants of health.
3. Application of systems thinking in public health
In recent years, there has been growing interest in the application of systems thinking to public health; and systems thinking and systems methods are identified as a key requisite capacity and capability of public health practitioners of the future (Erwin & Brownson 2017). Like the concept of systems thinking more broadly, there are many different perspectives and interpretations about what systems thinking in public health are. There are three main ways systems thinking has been used in public health, which include:
1. Working across different systems to improve health
2. Recognising settings where health promotion takes place are ecological systems
3. Using theory, tools and practices from other disciplines and applying to health to better understand complex problems and how to respond to them.
These are described below.
Approach 1. Working across different systems to improve health
This approach recognises that many of the determinants of health lie in systems outside of the health system: in the food system, transport system, housing and economic systems for example. Taking a systems approach involves working with these other systems.
For example, Health in all Policies is a public health intervention that attempts to create a joined-up government with an explicit health focus. It is one specific strategy to enhance population health by introducing health considerations into the decision-making of non-health sectors (Carey, et al. 2015). It can be pursued from a community-level, which has been done in Finland, or in contrast, from a top-down approach, which has been done in South Australia (Carey, et al. 2015).
There is extensive information on the South Australian approach to Health in All Policies on the Government of South Australia’s website, which you may like to have a look at: http://www.sahealth.sa.gov.au/wps/wcm/connect/public+content/sa+health+internet/health+reform/health+in+all+policies
Approach 2. Recognising that the settings where health promotion takes place are ecological systems
Ecological systems thinking has a long history of health promotion. This approach sees schools, worksites and communities (also known as ‘settings’), for example, as ecological systems in themselves.
Previous ways of working in this space simply treated schools, worksites and communities as venues to access people and deliver interventions based on individual change processes only. However, this has evolved whereby the systemic characteristics of these settings (the interactions and dynamic complexity) are better understood and acted upon in order to bring about systems-level change.
Ophelia is a health literacy initiative that aims to improve health and equity by increasing the availability and accessibility of health information and services in locally-appropriate ways. It was and remains a great example of a settings based approach that aims to bring about systems-level change.
| Subject | Nursing | Pages | 7 | Style | APA |
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Answer
Population versus Public Health
Part 1
Selection and information biases often occur in research activities where they involve a kind of errors motivated by researchers` decisions on who is to be investigated. Selection biases are associated with research where participant selections fail to assume randomization (Munafò et al., 2018). Similarly, research may portray aspects of information bias, a distortion in the measure of association triggered by a lack of accurate measurements of crucial study variables (Smith & VanderWeele, 2020). Ferrari et al. (1999) investigate travel as a risk factor for venous thromboembolic disease using a case-control study. Controlled case studies are prone to selection and information biases, especially in selecting subjects for the control group from populations that are not true representatives of populations that produce the cases.
In the Ferrari et al. (1999) study, however, incidences of selection and information biases are dealt with. The study averts selection bias or errors through the exclusion of patients with severe diseases. It recognizes that patients with severe comorbidities may have limitations of travel, thus offer little information relative to the development of inference from the research objective. Ferrari et al. (1999) recognize that averting information bias would contribute to a true definition of the inference and thus uses a standard measurement for all the patients (Krautenbacher et al., 2017). The study similarly collects information from the groups that are compared.
Issues associated with confounding biases negatively impact the reliability of studies in defining the research and researchers must aim to mitigate them through randomization, restriction, and matching (Schubauer-Berigan et al., 2020). Controlling for confounders can also entail adjustments after a study's completion using stratification or multivariate analysis. In the present study, issues of confounders are addressed in the data collection phase using restriction and marching. The study's approach only focuses on patients hospitalized in the cardiology department, which is crucial in eliminating confounders.
Question: Does controlling for selection and information bias in a case-controlled study positively contribute to the reliability of inferences? How do matching and restrictions compared to multivariate analysis in the process of controlling the effects of confounders?
Part 2
Integration of systems thinking in public health forms one of the most important developments in recent years. It plays a fundamental role in ensuring that all components of health interconnected. From my perspective, through learning systems, thinking is the core of current and future public health (Leslie et al., 2018). Systems thinking advances the idea of wholeness and interaction in public health, implying that in the development of solutions to public health issues, correspondences must recognize that the interconnection of parts affects outcomes. Professional systems thinking in public health allows identification of patterns of issues, openness, creates purposefulness in approaches, and introduces multidimensionality (Kreitzer et al., 2019).
Unless professionals in public health understand the dynamics of systems thinking, it becomes impossible to work in different systems to improve healthcare outcomes. Systems thinking develops a counterintuitive approach to practice and improve problem-solving abilities. Observing the emerging trends of healthcare underlines the need for professionals to expand abilities that support systems thinking. It is a crucial component that can support inter-professional practice in healthcare to promote the quality and accessibility of care (McNab et al., 2020).
Question: There is a growing demand for integrating systems thinking in public health practices to achieve better outcomes, including quality and access to care. What roles can healthcare policies play in enhancing the integration of systems thinking?
References
Ferrari, E., Chevallier, T., Chapelier, A., & Baudouy, M. (1999). Travel as a risk factor for venous thromboembolic disease: a case-control study. Chest, 115(2), 440-444.
Krautenbacher, N., Theis, F. J., & Fuchs, C. (2017). Correcting classifiers for sample selection bias in two-phase case-control studies. Computational and mathematical methods in medicine, 2017.
Kreitzer, M. J., Carter, K., Coffey, D. S., Goldblatt, E., Grus, C. L., Keskinocak, P., ... & Valachovic, R. W. (2019). Utilizing a Systems and Design Thinking Approach for Improving Well-Being within Health Professions' Education and Health Care. NAM Perspectives.
Leslie, H. H., Hirschhorn, L. R., Marchant, T., Doubova, S. V., Gureje, O., & Kruk, M. E. (2018). Health systems thinking: A new generation of research to improve healthcare quality.
McNab, D., McKay, J., Shorrock, S., Luty, S., & Bowie, P. (2020). Development and application of ‘systems thinking 'principles for quality improvement. BMJ open quality, 9(1), e000714.
Munafò, M. R., Tilling, K., Taylor, A. E., Evans, D. M., & Davey Smith, G. (2018). Collider scope: when selection bias can substantially influence observed associations. International journal of epidemiology, 47(1), 226-235.
Schubauer-Berigan, M. K., Berrington de Gonzalez, A., Cardis, E., Laurier, D., Lubin, J. H., Hauptmann, M., & Richardson, D. B. (2020). Evaluation of Confounding and Selection Bias in Epidemiological Studies of Populations Exposed to Low-Dose, High-Energy Photon Radiation. JNCI Monographs, 2020(56), 133-153.
Smith, L. H., & VanderWeele, T. J. (2020). Simple sensitivity analysis for control selection bias. Epidemiology.