The health policies are framed to improve the health care service deliveries that are important to improve the health outcomes of the population. The health policy framing requires every aspect of the disorder need to be included during the policy framing to improve the perceptive of the policy. The incidence rate is the actual presentation of the burden of the diseases over the population that help to understand the intensity of the disorder. The incidence rate is the core factor while designing the policy so that actual intervention can be designed to reduce the incidence rate of the disorder (Xie et al., 2018). The incidence rate help in the policymaking process as they help to correlate the new cases to understand the actual presentations of the disorder. The incidence rate presentation during the policy advice help to in the public health monitoring that is important for the addressing the issue associated with the disorder that can be depicted by incidence and prevalence rate (Boshuizen et al., 2017). The incidence rate of obesity will help to improve the understanding of the State Minister of Health concerning the burden of childhood obesity.
There has been a massive issue in the health care sector due to the pandemic as it has directly increased the mortality rate worldwide. There has been a continuous risk assessment for understanding the different COVID aspect to improve the management process of the pandemic (Bedford et al., 2020). The prevalence rate of the COVID will help to improve the management process of the infection so that the mortality rate can be decreased and the strength of the population can be improved to increase the resistant. The prevalence of the COVID-19 will help to improve the understanding concerning the infection as it combines the statistics from the old and new case to overall understand the impact of the infection (Phua et al., 2020). The prevalence rate help in the management process as it helps to identify the whole population associated with the disorder so that management process can be planned accordingly to address the whole population to reduce the burden of the disorder. The prevalence rate of the COVID-19 will help to improve the working pattern that is important to reduce the transmission that will help to manage the prevalence rate of the infection (Hamid et al., 2020). The prevalence rate will help to provide the right advice to plan the management of the COVID-19 prevalence.
One of the observational studies that can be utilized to understand the relationship between the work-related and mental health is descriptive studies that are population-based which include health survey. The descriptive study helps to understand the different variables that are associated with the study (Aggarwal & Ranganathan, 2019). Descriptive study design utilizes the systemic approach to discuss the phenomena by not controlling any variable that helps to identify the actual state of the phenomena without any biases. It correlates the different aspects that are associated with the phenomena to picture the situation (Rezigalla, 2020). There are the different method that can be used to improve descriptive study results which include case report, case series and cross-sectional surveys. It helps to describe the data related to study phenomena and its different aspect that are associated with the different characteristics of the individual (Ranganathan & Aggarwal, 2018). The Morton et al. (2016) descriptive studies have greater application in the researcher like it help to improve health care planning, hypothesis generation and trend analysis that are major research study aims. The descriptive study doesn’t utilize the control group in the researcher that helps to reduce the interference and improve the direct perspective of the researcher.
There different barriers that are associated with the data collection of the surveillance at the community level and one of them is the inconsistent data. The inconsistent data that is obtained from the surveillance at the community level is unable to combine and collect the relevant information from the surveillance. The inconsistent data decrease the chances of the accurate result as some of the data got missed due to the discreet form. Another barrier that directly increases the complication in the data collection of the surveillance at the community level is the lack of skills of the workforce. The lack of skills of the workforce increases the chances of mixing up the data that can directly hamper the results that are linked with the surveillance data (Panhuis et al., 2014). The quality assurance is another barrier that is associated with the data collection gathered from the surveillance. The lack of quality assurance in the data increases the chances of incorporation of the hampered data which lead to the poor study results. The community-level surveillance data is complex thus data collection process become hectic thus it hampers the results analysis process (Williams-Roberts et al., 2018). These barriers increase the complication of the data collection process related surveillance data collected at the community level.
Aggarwal, R. & Ranganathan, P. (2019). Study designs: Part 2 - descriptive studies. Perspectives in Clinical Research, 10(1), 34–36. https://doi.org/10.4103/picr.PICR_154_18
Bedford, J., Enria, D., Giesecke, J., Heymann, D. L., Ihekweazu, C., Kobinger, G. & Wieler, L. H. (2020). COVID-19: Towards controlling of a pandemic. The Lancet. 395(10229), 1015-1018. https://doi.org/10.1016/s0140-6736(20)30673-5
Boshuizen, H. C., Poos, M. J., van den Akker, M., van Boven, K., Korevaar, J. C., de Waal, M. W., Biermans, M. C. & Hoeymans, N. (2017). Estimating incidence and prevalence rates of chronic diseases using disease modeling. Population Health Metrics, 15(13), 1-14. https://doi.org/10.1186/s12963-017-0130-8
Hamid, S., Mir, M. Y. & Rohela, G. K. (2020). Novel coronavirus disease (COVID-19): A pandemic (epidemiology, pathogenesis and potential therapeutics). New Microbes and New Infections, 35(100679). https://doi.org/10.1016/j.nmni.2020.100679
Koon, A. D., Hawkins, B. & Mayhew, S. H. (2016). Framing and the health policy process: A scoping review. Health Policy and Planning, 31(6), 801–816. https://doi.org/10.1093/heapol/czv128
Morton, S. C., Costlow, M. R., Graff, J. S. & Dubois, R. W. (2016). Standards and guidelines for observational studies: Quality is in the eye of the beholder. Journal of Clinical Epidemiology, 71, 3–10. https://doi.org/10.1016/j.jclinepi.2015.10.014
Panhuis, W. G., Paul, P., Emerson, C., Grefenstette, J., Wilder, R., Herbst, A. J., Heymann, D. & Burke, D. S. (2014). A systematic review of barriers to data sharing in public health. BMC Public Health, 14, 1144. https://doi.org/10.1186/1471-2458-14-1144
Phua, J., Weng, L., Ling, L., Egi, M., Lim, C.-M., Divatia, J. V. & Du, B. (2020). Intensive care management of coronavirus disease 2019 (COVID-19): Challenges and recommendations. The Lancet Respiratory Medicine. 1-9. https://doi.org/10.1016/s2213-2600(20)30161-2
Ranganathan, P. & Aggarwal, R. (2018). Study designs: Part 1 - An overview and classification. Perspectives in Clinical Research, 9(4), 184–186. https://doi.org/10.4103/picr.PICR_124_18
Rezigalla A. A. (2020). Observational study designs: Synopsis for selecting an appropriate study design. Cureus, 12(1). https://doi.org/10.7759/cureus.6692
Williams-Roberts, H., Neudorf, C., Abonyi, S., Cushon, J. & Muhajarine, N. (2018). Facilitators and barriers of sociodemographic data collection in Canadian health care settings: A multisite case study evaluation. International Journal for Equity in Health, 17(186), 1-10. https://doi.org/10.1186/s12939-018-0903-0
Xie, Y., Yang, J., Jiang, C., Cai, Z. & Adagblenya, J. (2018). Incidence, dependence structure of disease, and rate making for health insurance. Mathematical Problems in Engineering, 2018, 1–13. https://doi.org/10.1155/2018/4265801
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