PUBH6005 Epidemiology

Introduction

The article entitled “Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016” by Fitzmaurice Christiana investigates prevalence of cancer and other communicable diseases and its effects on human development (Fitzmauric, 2018, pp. E2). The article specifically aims to evaluate burden for twenty-nine groups over a specific time to offer a model for resource allocation, policy deliberation, as well as research focus. The study underscores the importance of the research given the growing prevalence of cancer and other communicable diseases (Foreman, Lozano, Lopez & Murray, 2012, pp. 1). Many studies have shown that only in 2016, there were around 17.2 million cases of cancer disease globally, as well as 8.9 million mortalities. Therefore, to find the pertinent results that will answer meet the aims of the stud, there is the need to develop effective research methodology and analysis to answer the research questions. This analysis will explore the research methodology and data analysis method that was used by the author.

Methodology

The article used Global Burden of Disease (GBD) study estimation technique as quantitative assessment tool of the cancer burden of cancer in the group under study.  The method in this study specifically reestimated the time series of each study before the GBD, where the results in the study superseded previous GBD studies. The estimation method used defined all cancers based on the International Classification of Diseases (ICD) and categorized them into 29 groups (Fitzmauric, 2018, pp. E2). Furthermore, changes from the GBD 2015 study comprise novel data additions of “other leukemia” being the primary cause, alterations in the mortality-to-incidence ratio (MIR) assessment and reporting estimates for nonmelanoma skin cancer disease (NMSC). Therefore, the study estimated nationwide illness burden for 195 nations plus regions for the GBD 2016 (Begg, Vos, Barker, Stanley & Lopez, 2008). Thus, the estimates of the GBD 2016 were consistent with the GATHER guidelines and global burden disease on global population standard was employed in the study for the computation of age-standardized frequencies. Thus, the method estimated the input of populace aging, population growth, as well as modification in age-specific frequencies on the modification in incident cases amid 2006 and 2016 (Fitzmauric, 2018, pp. E2). The estimation method used reported 95% uncertainty intervals (UIs) for the entire estimates.

Analysis

The researchers in the study analyzed the trends and levels over time, and by the Sociodemographic Index (SDI). The modifications in the occurrence were grouped by the alterations in levels and trends because of epidemiological vs. SDI. The analysis adequately stratified the results employing SDI quintiles. The SDI provided a better analysis of the data collected through the estimates because it is a composite indicator that includes education, income, as well as fertility that are important measures of GBD that will show a relationship well with the health outcomes (Fitzmauric, Allen & Barber, 2017, pp. 524).

Appropriateness of Research Methods

The method that was used in this study based on estimates was appropriate in investigating the burden of cancer disease in different countries. The GBD study estimation method was appropriate because it helped to address issues that emerge when data exists in diverse forms through converting the findings to comparable formats. This was achieved through employing empirically observed associations between the reference format and non-standard formats (Anderson & Duggan, 2016, pp. 84). In addition, the methods used in the article can have applications outside the GBD analysis, for instance, in systematic reviews of public interventions regarding or other development interventions in which the indicators are diverse in the case of cancer burden. Accordingly, the method of GBD study method that used estimates was appropriate for the study because the study entailed estimates of 29 groups in 195 countries that could be complex when using other qualitative methods (Fitzmauric, 2018, pp. E2).  However, to further improve the reliability and credibility of data collection, there is the need to develop and expand cancer registries, health surveys, registration systems, and coding systems. This will improve the data collection and analysis providing results that are more valid and accurate.

Conclusion

The GBD study estimates offered better method to collect data that was effective in the development of interventions of cancer control and resource allocation. The estimates were adequate in providing information that is applicable plus current on the international regional along with countrywide problem of cancer and other non-communicable diseases (GBD 2016 Causes of Death Collaborators, 2017, 1151). Therefore, the estimates were effective in filling the gap in which authentic information regarding the disease burden are inaccessible or sparse.

References

Anderson, B.O. & Duggan, C. (2016). Resource-stratified guidelines for cancer management: correction and commentary. Journal of Global Oncology. 3(1):84-88.

Begg, S. J., Vos, T., Barker, B., Stanley, L & Lopez, A.D.  (2008). Burden of disease and injury in Australia in the new millennium: measuring health loss from diseases, injuries and risk factors. Medicine Journal of Australia. 188(1): 36-40.

Fitzmauric, C. (2018). Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study. JAME Oncology. 3(1): E1-E16.

Fitzmauric, C., Allen, C. & Barber, R.M. (2017). Global Burden of Disease Cancer Collaboration. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the Global Burden of Disease Study. JAMA Oncology. 3(4): 524-548.

Foreman, K.J., Lozano, R., Lopez, A.D. & Murray, C.J. (2012). Modeling causes of death: an integrated approachusing CODEm. Population Health Metrics. 10(2):1.

GBD 2016 Causes of Death Collaborators. (2017). Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet. 390(10100):1151-1210.