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General Overview

Aging Analytics Agency has created its own semi-automatic SWOT analysis tool for assessing the level of development of the country's longevity status and initiatives. Users of the tool, government officials or stakeholders, can assess the current level of longevity development within a particular economy and obtain appropriate recommendations.

The semi-automatic SWOT for providing recommendations to the country on the development of the Longevity industry includes consistent and logical assessments of quantitative and qualitative indicators of the country being evaluated, with appropriate recommendations depending on the level of assessment that the country receives in each of the categories included in the assessment of quantitative and qualitative indicators.

The assessment includes quantitative and qualitative indicators by which the country is evaluated, and contains +200 metrics divided into 7 categories. Each category was provided with recommendations for the development of the Longevity in the country, depending on the assessment of the country in each category, a number of recommendations are selected that can be applied to this country. The final decision on whether to adopt or not to adopt a country-specific recommendation is provided in the Decision Table.


The tool is divided into 3 main parts: Data input, Evaluation, Recommendations.


1st Part. Datainput. Filling in all the necessary data for all countries.

The Data Input Part contains a  table which is structured into 7 main categories:

  • Health Status

  • Government care

  • Gov policy

  • Demography

  • Society

  • Ecolog

  • Economy

A total of +200 evaluation metrics is given in the table. 


The data for all metrics may be found in open-source databases of international organizations related to Healthcare and Longevity respectfully. Main data sources include:



2nd Part. Data Evaluation. All the data is being processed, evaluated and weighted due to the importance and correlation with longevity.

The Data Evaluation Part contains an assessment table which assigns a score for each metric for each country. The score is formed by analyzing data provided in the Datainput Part.

Each set of data for each metric from the Datainput Part is divided by normal distribution method into three groups in order to find out the allocation of a particular country’s metric within the analyzed set. According to the group in which a certain metric of the analyzing country falls, it is given the following scores: 1, 2, 3 for the bad, normal, and good condition of the indicator, respectively. For example, If health-adjusted life expectancy (HALE) is less than 63 years, then the score is 1, if HALE is more than 63 but less than 70, the score will be 2 and if HALE is higher than 70 then 3 must be assigned.

Afterwards, all data moves to the Assessment Table.

In the assessment table the user selects a country for which he wants to receive a set of recommendations. All points extracted from the Data input Part sum up into the general score on a category.

Each category ends up with the line “Category score”, reflecting the total score of the country in that category. Results interpreted as "weak", "satisfactory" or "strong". These scores were determined by applying a uniform distribution to a number of possible values ​​of the overall score of the category. According to the assessment of the category, the country under analysis is provided with a number of recommendations that were developed by the AAA team.


3rd Part. Recommendations. The pool of recommendations was developed by Aging Analytics Agency professionals for governments varying in level of economic development. The full list of recommendations consists of 107 recommendations divided by earlier mentioned categories and divided by the level of development of the longevity in the category. 


Depending on the category score the relevant recommendations appear on the Decision table. The assessment of the categories results in “weak”, “satisfactory” or “strong” estimates.


If the score for the category is less than 33% of the maximum possible result then the category is rated as weak, consequently, fundamental issues are to be solved. In this case, we will point out on modernization of equipment in public hospitals, even distribution of medicines and progressive equipment among the regions or similar. If the category result is within 33-67%, then recommendations from the “satisfactory” pool are applied mostly. It includes the development of government-led longevity plans, the Creation of an effective network of primary care services, and others.  For the categories, which received above 67% of points, and are rated as strong, most of our recommendations come from a respective list, including the Development of AI centers, Broadening the infrastructure of financial institutions that contribute to Longevity, Behavioral based health advising, etc.

Some recommendations are relevant and applied for both “weak” - “satisfactory” or “satisfactory” - ”strong” estimations of the country’s longevity. Examples of such recommendations are improvement of engagement of high-qualified staff in healthcare, provision of more freedom for private sector healthcare development, support of healthy and disease-free lifestyles with an emphasis on the health status of the elderly.

The culmination of the Tool is the Decision Table. It shows summary statistics on the level of the development of Longevity by categories and presents the list of relevant improvements.

Summary statistics are presented in the table named “Results” showing the actual estimations as a number of points per category out of a maximum possible and its wording variant (weak, satisfactory, strong).

Below the summary table, the full list of AAA recommendations is placed. The list consists of 107 recommendations divided into the same 7 categories as it was mentioned above. 

It is organized in such a way that each recommendation has its number (ID) the recommendation itself and a detailed description of the actions that are to be taken. 

The decision table is the final step in our tool and it already shows personalized recommendations to the user by highlighting the relevant ones in green color. Those that are not applicable are colored in red.

In other words, the Decision Table provides a user with a full list of developed recommendations with explanations and indicators of whether to apply or not to apply them for the country being analyzed. 

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