Epidemiological data play an important role in healthcare policy making

Epidemiology is a branch of study that predicts the occurrences and patterns of diseases in different groups of the population. It helps in assessing the reason and factors behind the occurrence of a disease. Epidemiological data helps to plan and strategies to prevent and manage epidemic diseases or illness. Epidemiological findings thereby help Government and healthcare organisations to implement methods of prevention, intervention and policymaking. In addition, it also helps the pharmaceutical industries to assess clinically if the intervention methods or drugs or treatment methods are efficient enough for eradication and prevention of the disease. Epidemiology study is based on statistics and the evaluation of data in the implementation of policies and the control of infectious diseases.

Classification of statistical applications in an epidemiological study

Statistical analyses of epidemiological data are presented empirically, graphically, or in pictorial format. Interveners of epidemiology comprise of members from the pharmaceutical, healthcare industries, political epidemiologists and the policymakers of India. They form an integral part in healthcare based public policies. Formation and amendment of healthcare policies need data, statistics, and its outcomes. The interveners use different data, statistics, and interpret its outcomes differently.

Classification of tools for statistical analysis of epidemiological data

In a health report for the National Rural Health Mission (NRHM) by the Ministry of Health and Family Welfare in 2017, used rural public economic and financial data to assess the public’s capability to comprehend various forms of diseases. This allowed the Government to recommend and allow better expenditures for the rural population who cannot afford quality treatment for the severe disease (Directorate General Of Health Services, 2018).

Various tools are used for epidemiological data analysis that helps in the assessments of various disease occurrences. Mortality data and disease incidences are the most commonly used data to analyse infectious disease prevalence by healthcare agencies and the policymakers. However, the Government may also use economic and financial data as they play a major role in control and management of diseases and implementation of policies. Similarly, healthcare and pharmaceuticals use drug-based data and clinical data aimed to control and management of infectious diseases.

High-end software and tools such as R-package, SAS, Minitab, Python and other similar languages and statistical programs are used by pharmaceuticals and healthcare agencies. Econometric tools such as STATA and SPSS are widely used by interveners that are involved in the application of epidemiological data for disease management and policymaking. The policymakers, however, use personalized tools specifically made for reviewing the disease prevalence and reviewing its policy or to implement a new policy (Jewell, 2004).

The Indian Government uses SAS, Redcap software developed by Vanderbilt University, and other personalized software developed for the National Institute of Epidemiology, India (National Institute of Epidemiology, 2018).

Different types of graphical presentation of epidemiological data

In a report for the National Health Profile (NHP) of India in 2018 by the Directorate General Of Health Services, (2018), India used state wise demographics for different factors causing the prevalence of Malaria.

The graph presents the population density in India over years 1901 to 2011. Similar graphical presentations were also used in the report for identification of disease prevalence and factors causing them.

Different types of charts and graphs are used by political epidemiologists, policymakers, healthcare, and pharmaceuticals in order to help in policy development, and control of infectious diseases. Statistical outcomes are used by policymakers and healthcare members in the forms of charts and graphical presentations for understanding the prevalence of diseases (Merrill, 2015). These graphical outcomes indicate the trends presentable as frequency over time from these graphs. Frequency in the graphical outcomes indicates the tally of disease cases and incidences.

The epidemic curve is one of the advanced forms of graphical presentation used by the Governments and healthcare to display the onset of illness among cases associated with an outbreak (Merrill, 2015). The graphical presentations use mortality data, interventions data and incidence data that presents the outbreak of infection over a specific time period. The following image presents an exclusive example of the use of an epidemic curve over a specific time. In the same report for National Health Profile (NHP) of India in 2018 by the Directorate General Of Health Services, (2018) used the epidemic curve to show the prevalence of cases and deaths from Malaria over the years. The epidemic curve graph is clear on the presentation of epidemiological data of  Malaria and its epidemic data as of how the case incidences prevail over time and period.

Policymakers, healthcare and pharmaceuticals also use bivariate scatter plot and spot map for the presentation of the prevalence of infectious diseases over time and in a specific geographic location. This helps to access the potential of the disease management protocols and policies and implement better strategies for disease management. This type of presentation is more associated with geospatial analysis whereby the graphical presentations are presented in the form of maps. The data comprises of mortality, incidences, and type of infection over time and place. The graphical presentation clearly shows the prevalence of Malaria and its related demographics over a geographical location using a map and scatter plot. The following graphical presentation was used by CDC, (2016) in a study to show the prevalence of malarial demographics in India. The CDC used malarial cases for each state and mapped the prevalence over time.

In addition, there are other forms of presentation of graphical presentations from the epidemiological data analysis. Forest plot and effect size graphs from Meta-analyses used by pharmaceuticals in finding the efficacy of the drug-based interventions and present the change or difference in the intervention (Ganeshkumar, et. al., 2018). They mainly comprise clinical and population-based data whereby the variables of drug efficacy and outcome variables are presented. They are also used by the policymakers to check the efficacy of policy-based interventions. The data mainly comprises of economic and financial data and disease-based demography data. The following image shows an example of the use of outcome from disease interventions, whereby the graph presents the comparison between different intervention methods. The points within or closest to the centre line, show efficiency to the intervention method.

In a study on dengue infection in India for the Government of India Ganeshkumar et al., (2018),  presented systematically case fatality and its prevalence from Meta-analysis of epidiemological data.

These graphical presentations use different forms of epidemiological data and variable. Although graphs can be attained from different types of tools and software, the presentation of graphs and usage of data types remain the same. However, the use of the graphs is based on the type of outcome required by the intervener. For instance, to check the efficacy of healthcare expenditures one may use ANOVA and regressions or may even use meta-analysis. ANOVA and regressions will present the trend and impact of expenditure over time, but meta-analysis will show the efficacy of the healthcare expenditure and plan accordingly for the future. Therefore the needs of the presentation of graphs are based on the application of statistical outcomes.

Application of epidemiological data to control and manage EID

Emerging infectious diseases (EID) are caused by viral or bacterial pathogens causing diseases that may be communicable or non-communicable. Common EIDs are HIV, Malaria, Zika virus and many others to name. The rise in the social, political, and environmental crisis globally has a paved path for the emergence of various infectious diseases. Epidemiological studies have been helpful in assessing the trends and the spread of infectious diseases, such as Malaria (Formenty, 2014). Epidemiological studies for infectious diseases have also been helpful in containing the diseases to one geographic location. Infectious diseases have a high chance of re-occurrence or re-emergence over the years, and in such cases, epidemiological data analysis help in prompt intervention to quarantine the diseases.

Epidemiology has been helpful not only to prevent diseases but with immunization, and vaccination processes of infectious diseases. Epidemiological studies have also been helpful in a classification of non-communicable diseases and communicable diseases to plan for treatment and intervention mentions accordingly (Rathore et al., 2017).

Political epidemiologists’ application of epidemiological data based on a nation level control and management of diseases, proper allotments of finances, expenditure, check the efficacy of various health care programmes, and implementation of new policies or modify the existing one (Rathore et al., 2017). Furthermore, Governments’ application of epidemiology are also based on national-level control and management of diseases, proper allotments of finances, expenditure, check the efficacy of various health care programmes, and implementation of new policies or modify the existing one.

However, a healthcare institution primarily applies policies in the state and district level to control and manage diseases and drug intervention. Secondary applications include helping in ground-level achievement (rural and district level) of policy implications by the Government and management and control of diseases. They also use epidemiology for retrieving data and analysis on behalf of the Government and other authorities (Rathore et al., 2017). Secondarily they also help the Government to identify the variables and other related causes of the prevalence of infections, along with the production of geographic-based personalized drugs for control and prevention of diseases from the efficacy of drugs.

The application of epidemiological in assertation, implementation and prevention

Epidemiology’s contribution to public health policy is not only aimed at the identification of interventions, diseases and the population but also evaluation of cost-reduction pressures, accessibility to care around different grades of the population, and providing equal health care benefits. Epidemiology in public health policies helps the Government to control infectious diseases and acute and long-lasting conditions amongst the diverse population.  It further helps to assess biologic, environmental, social, behavioural, and health care factors that lead to endemics and thereby strategize disease management, allocate health care expenditures and prepare for future re-occurrences (Barata, 2013).

The political strategists may imply political epidemiology and help the Government to address the issues and plans to control the disease. The political strategists also consider the data and outputs from the pharmaceuticals towards devising new drugs against the viral or bacterial cause identified (Rathore et al., 2017). This helps in stricter policy-making even for the pharmaceuticals towards usage and manufacturing of new drugs and associated clinical tests.  Similarly, in the case of poor infrastructure, the political strategists help the Government in the identification of the factor. This leads to the implementation of the policy that focuses on better living and lifestyle and improvement of proper sanitation. Political strategists use the economic and financial data in order to help the Government to plan for efficient usage of funds and expenditure for development of public healthcare and improve the existing policies with respect to the overall economic development of the country.

In this regard, healthcare uses various strategies for identifying the causal reason for the prevalence of a particular disease. Collection of data on post environmental changes, such as climate or weather anomalies, natural disasters, ecological (flood, earthquake) or manmade conditions (war), helps the policymakers in assessing the potential to control and manage prevalence of infectious diseases. The data also helps healthcare agencies in assessing and forecasting the prevalence of diseases and ways to implement Government policies to manage emerging infectious diseases. According to Rathore et al., (2017), travel and commerce have also been a major cause of rising of infectious diseases in India, and it again helps the healthcare to assess potential infections being carried away from or into the country from other geographical locations.

In the case of pharmaceutical, they directly assess statistically the efficiency of drug-based intervention methods for the control of infectious diseases. The emergence of the antibiotic-resistant bacteria or viral organism leads to the rise in the prevalence of infections. Control measures can be developed when the pharmaceutical industry uses epidemiology to learn about the cause of the rising of the diseases. Natural calamities and man-made changes lead to chaos and lack of medical healthcare (Ministry of health and family welfare, 2018). Infectious diseases arising from this is used by pharmaceutical members towards the statistical identification of the source and then implements methods feasible for drug development.

Using epidemiological data for studying the prevalence of Malaria

Malaria has been found to be the most prevalent and consistent infectious disease in India for over 70 years. It has also been found that drug-resistant strains of malarial infections are also becoming prevalent in India. Therefore, epidemiological data for Malaria will be chosen and the results of the analysis will help assess the prevalence of the disease. The outcomes will help to understand how Government applications in the control and management of Malaria and policy implications. Malaria in India is prevalent due to lack of living standards and low income, lack of awareness, low sanitation and hygiene, poor infrastructure, and natural calamities like flood and drought (Ministry of health and family welfare, 2018). Over the years, the percentage of Malaria incidences in India has decreased. This is contributed by increased engagement of the Government of India. Improved expenditure for public healthcare as a whole, revised healthcare policies, and rural healthcare development programmes are the main engagements by the (Directorate General Of Health Services, 2018).

Use of epidemiology has helped the Government to improve expenditure for public healthcare as a whole, revised healthcare policies, and public healthcare development programmes (Planning Commision, 2017). The planning commission of India also expedites the expenditures for public health comprehending prevalence of Malaria cases. It allows the healthcare to effectively provide health care services to the public mainly the poor and the rural. Revised policies and rural health care programmes help the pharmaceutical industries to develop new drugs on the basis of the economic feasibility of the rural and poor population of India. Better accessibility of drugs by pharmacy industry and treatment methods by healthcare reduces the case of Malaria incidences. This provides relevance to the Government on the efficacy of the policies implemented and thereby update the existing or create a new policy.

Pharmaceuticals and healthcare industries use their own methods to study epidemiological data for effective drug development and provision of treatment. Therefore, trend-based assessments, correlations, regressions, meta-analyses, forecasting, and geospatial analyses used by policymakers, healthcare agencies, and pharma industries will be studied in depth. Trend-based analyses and geospatial analysis will use malarial demographics to help indicate the prevalence of Malaria in India and thereby indicate the efficacy of intervention methods implemented by the Government. Correlations and regressions will use Malaria demographics and healthcare expenditures data to assess the efficacy of Government expenditures in managing Malaria.

The meta-analysis will focus to present the efficacy of different policies and their role in reducing the cases of Malaria in India. Data will be divided on the basis of the malarial demographics during the policy implementation. This will also implicate the efficacy of the healthcare and the pharmaceuticals in the provision of quality treatment methods and drugs for control of Malaria prevalence and their role in helping the Government achieve its healthcare objectives. Similarly, forecasting will help understand the efficacy of the Government in achieving its target of reducing Malaria cases prevalence in India. Moreover, it will also help explore the change in responsibilities of the Government, healthcare and pharmaceuticals with respect to planning for intervention methods to achieve the forecasted values in real-time in the future.

Policymakers use epidemiological data from primary and secondary sources to assert the efficacy of intervention methods against infectious diseases. The assessment helps the policymakers to implement new and better policies, which are considered by the pharmaceuticals in an attempt to prevent re-emergence and prevalence of infections and its strains by the development of new and efficient drugs. The healthcare industries then use the drug to administer to the needful and collect data on the efficacy and management of the infections. The data of incidences are again used by the policymakers to assess the efficacy of the interventions. Thus, assertion, implementation, and prevention are responsible for the control and management of infectious diseases and policy implementations.

References

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• Directorate General Of Health Services, 2018. National Rural Health Mission (NRHM) report 2017 [WWW Document]. URL http://dghs.gov.in/.
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• Ganeshkumar, P., Murhekar, M. V., Poornima, V., Saravanakumar, V., Sukumaran, K., Anandaselvasankar, A., John, D., Mehendale, S.M., 2018. Dengue infection in India: A systematic review and meta-analysis. PLoS Negl. Trop. Dis. 12. https://doi.org/10.1371/journal.pntd.0006618.
• Jewell, N.P., 2004. Estimation and Inference for Measures of Association, in: Statistics for Epidemiology. pp. 76–97.
• Merrill, R. M. (2015) Introduction to Epidemiology. Seventh. Jones & Bartlett Publishers.
• Ministry of health and family welfare. (2018). National Health Profile (NHP) of India- 2018 :: Central Bureau of Health Intelligence. Retrieved August 30, 2018, from http://www.cbhidghs.nic.in/index1.php?lang=1&level=2&sublinkid=88&lid=1138.
• National Institute of Epidemiology, 2018. Epidemiology- Tools. Retrieved September 5, 2018, from http://www.nie.gov.in/.
• Planning Commission, (2017). Five year plan. https://planningcommission.gov.in/.
• Rathore, M.H., Runyon, J., Haque, T., 2017. Emerging Infectious Diseases. Adv. Pediatr. 64, 27–71. https://doi.org/10.1016/j.yapd.2017.04.002.