Introduction

One of the tasks of forming the activities of medical organizations and predicting the values of indicators in the health sector is the creation of tools to determine and measure the level of influence of each individual medical organization on health indicators1. Therefore, the scientific approach to the assessment of morbidity and the organization of medical and preventive measures is one of the main tasks of the management of state and territorial legislative and executive authorities, which determines the policy of health protection, economic, legal, and social measures. Thus, the ability to manage health indicators through improving the work of the system of governing bodies of medical organizations allows timely finding sufficient resources, primarily financial, for areas that contribute to achieving maximum efficiency with minimal use of resources (increasing the efficiency) in the management of activities and the performance of medical organizations².

Methods

The following reporting forms were selected for federal statistical observation in healthcare: No. 12 "Information on the number of diseases registered in residents of the service area of a medical organization" for 2010-2019, “Distribution of deaths by sex, age groups and causes of death” mortality tables, demographic yearbooks of the Chuvash Republic, published by the Territorial Body of the Federal State Statistics Service in the Chuvash Republic, as well as data from demographic yearbooks published by the Federal State Statistics Service for the corresponding years^3,5.

The correlation coefficient was calculated between general morbidity and mortality in the Chuvash Republic. It is a selective linear correlation coefficient calculated by the formula:

The connections between signs can be weak and strong (close). Their criteria are assessed on the Chaddock scale: 0.1 < r_xy <0.3: weak; 0.3 < r_xy <0.5: moderate; 0.5 < r_xy <0.7: noticeable; 0.7 < r_xy <0.9: high; 0.9 < r_xy <1: very high.

The administrative-territorial regions of the Chuvash Republic were grouped by levels of mortality and general morbidity using the mapping analysis. On the basis of cartographic analysis, the administrative-territorial regions are grouped according to morbidity and mortality^4,6,7,8,10:

1) We calculated the arithmetic mean of the relative indicators for the administrative-territorial regions of the Chuvash Republic for each analyzed year (national average data - M1-10) according to the formula M = ΣV/n, where M is the arithmetic mean; V is relative indicators of the administrative-territorial regions of the Chuvash Republic; n is the number of observations.

2) We determined the standard deviation of variations in the statistical series of average data for the regions of the Chuvash Republic by year: δ = √Σd2/n-1, where d is the deviation (the difference between the average value and each variation); n is the number of observations.

3) The intervals for grouping the administrative-territorial regions of the Chechen Republic by the level are presented: M-0.5δ ≤ M¹ ≤ M + 0.5δ - the indicator within the average republican data (average) for the Chuvash Republic; М + 0.5δ < М¹ ≤ М + 1.5δ - the indicator higher than the average republican data (high); М-1.5δ ≤ М¹ <М-0.5δ - the indicator lower than the average republican data (low); M¹> M + 1.5δ - ultra-high indicator; М¹ <М-1.5δ - ultra-low indicator.

4) We determined the average value of the administrative-territorial regions of the Chuvash Republic for the decade M¹1-M¹85 was determined according to the arithmetic mean formula.

5) We grouped the administrative-territorial regions of the Chuvash Republic according to the distribution M¹1-M¹25 according to the corresponding intervals.

We used our original methodology to evaluate the effectiveness of medical care in the administrative-territorial regions of Chuvash Republic⁹.

The numerical indicators of general morbidity and mortality in the Chuvash Republic were compared. The difference between the mortality and general morbidity in certain areas was clearly defined.

The ratio of morbidity and mortality by administrative-territorial entities is particularly important, especially in low morbidity areas. Thus, low morbidity and high mortality are an indicator of the effectiveness of primary health care.

The overall mortality/morbidity (SI coefficient) was calculated and the ratio of the averaged indicators of the general morbidity and general mortality was determined. A special coefficient, SI, has been determined for each administrative-territorial region of the Chuvash Republic, i.e. SI = the level of overall cardiovascular morbidity/mortality⁹.

Finding the special indicator (SI) allowed us to identify the subjects with the maximum level of the special coefficient, as well as with the minimum SI (administrative-territorial areas with ultra-low overall morbidity and ultra-high overall mortality).

To distribute the regions of the Chuvash Republic by the value of the special indicator, its intervals were determined. For this, ∆ has been calculated: ∆=(k_max-k_min)/4, where

k_max is the level of maximum SI of the administrative-territorial regions of the Chuvash Republic,

k_min is the level of minimum SI of the administrative-territorial regions of the Chuvash Republic.

The grouping intervals for the administrative-territorial regions of the Chuvash Republic have been determined according to the following method: superhigh SI group – SI higher than the average for the Chuvash Republic; high SI group: SI - ∆; medium SI group: SI - 2∆; low SI group: SI - 3∆; ultra-low SI group: SI - 4∆.

Thus, we used the exponential smoothing method (exponential average), discovered by Brown and Holt, to predict the indicators of overall mortality in the administrative-territorial regions of the Chuvash Republic and the prevalence of diseases. The formula for simple exponential smoothing is as follows:

St+1 = α ×Xt + (1- α)×St , where Xt is the actual value at a given point of the series .; St is the forecast at the point of the series .; α - some predetermined smoothing coefficient (from 0.1 to 0.9), constant throughout the row.

Thus, each new smoothed value was determined as a weighted average of the current observation and smoothed series. The smoothing result depended on the parameter α (alpha).

To build an exponential smoothing curve, the root-mean-square error of approximation (mean square of errors) was calculated as the root-mean-square deviation of the true values of the series from its approximation and the relative error of approximation in relation to the absolute error of approximation to the range of values of the series. The range was estimated as the difference between the maximum and minimum values of the series after excluding 10% of its largest and smallest values.

In practice, the smoothing parameter was searched for on the grid according to Gardner’s recommendations (1985). Possible values of the parameter were distributed by grid with a certain step. Value grid–from α=0.1 to α = 0.9, with a step of 0.1. Then α was chosen for which the sum of the squares (or mean squares) of the residuals (observed values minus one step ahead predictions) was minimal.

Mathematical processing of the obtained data was carried out using STATISTICA 6.0 and "Excel 7.0" on IBM-PC, confidence level = 0.95.

Results and Discussion

To assess the effectiveness of the outpatient-polyclinic link, the presence and the closeness of the relationship between the indicators of general mortality and morbidity from diseases of the circulatory system of the population of the Chuvash Republic were determined for the study period:

According to the Chaddock scale, the relationship between mortality rates and disease prevalence is noticeable and inverse = -0.689.

Considering the forecasts made up to 2029, the pattern of a decrease in mortality with an increase in the incidence of cardiovascular diseases is clearly visible.

Our map analysis of the incidence of cardiovascular diseases (BCS) among the population of the Chuvash Republic in 2010-2019 found its super-high level in the following administrative-territorial districts of Chuvash Republic: in Kozlovsky, Shumerlinsky, Poretsky, Alatyrsky, Yadrinsky, Yalchiksky, Yantikovsky districts, as well as in the cities of Alatyr and Shumerlya. Ultra-low levels fore found in Cheboksary, Tsivilsky, Vurnarsky administrative-territorial districts, as well as in the cities of Novocheboksarsk and Kanash.

Our map analysis of mortality from cardiovascular diseases of the population of the Chuvash Republic found its super-high levels in the following administrative-territorial districts of the Chuvash Republic during the study period: Kozlovsky, Shumerlinsky, Poretsky and Alatyrsky, high - in Shemurshinsky and Krasnoarmeysky. Ultra-low levels were found in Morgaushsky, Cheboksary administrative-territorial districts of the Chuvash Republic, as well as in the cities of Cheboksary, Novocheboksarsk, Kanash. Low mortality was noted in Tsivilsky, Yantikovsky, Vurnarsky, Ibresinsky, Komsomolsky and Batyrevsky districts of the Chuvash Republic.

Comparison of the numerical indicators of overall morbidity and overall mortality in the Chuvash Republic clearly showed the imbalance between these indicators in its individual administrative-territorial regions. We have found low level of overall mortality in some regions of Chuvashia, accompanied by a relatively high level of overall morbidity, and vice versa, a low overall morbidity is combined with a high level of overall mortality.

Thus, the quantitative values of the ratio of indicators of general morbidity and total mortality, SI, were determined for all administrative-territorial districts of the Chuvash Republic. In Yalchik, Yantikovsky, Morgaushsky, Cheboksary, Ibresinsky, Yadrinsky, Alikovsky, Komsomolsky, Mariinsko-Posadsky, Krasnochetaysky and in the cities of Cheboksary and Novocheboksarsk we have found an ultra-high SI levels that formed during the studied period. An ultra-low SI value was determined in the Kozlovsky, Krasnoarmeysky, Alatyrsky and Shumerlinsky administrative-territorial districts of the Chuvash Republic.

Summary

1. The numerical indicators of general morbidity and mortality in the Chuvash Republic clearly show the difference between the mortality and general morbidity in certain areas.

2. Low morbidity and high mortality are an indicator of the effectiveness of outpatient care and the quality of primary health care.

3.A mapping study of the ratio of morbidity and mortality from circulatory diseases in a particular region of Russia is useful for identifying the "weakest link" in the system of providing outpatient medical care, assessing the effectiveness of management measures at the level of regional authorities, assessing the performance of heads of medical organizations, developing the necessary set of measures, based on the characteristics of the level and trends of medical and demographic indicators with due regard to the leading principles of strategic planning.

4. An extremely high SI (overall mortality/morbidity ratio) indicates insufficient efficiency of the organization of medical examination, low provision with medical personnel and medical equipment, lack or insufficiency of primary care institutions in these constituent entities of the Russian Federation, including a decrease in the availability of medical care, and on the need for a thoughtful and clear approach to making appropriate management decisions in each administrative-territorial unit.

Conclusions

Analysis of the ratio of mortality and morbidity from diseases of the circulatory system makes it possible to single out the administrative-territorial districts of the constituent entities of the Russian Federation, where, first of all, it is necessary to assess the quality of medical care and develop a specific plan to achieve health indicators defined as indicators of the implementation of the state "Health Development” program, regional and municipal plans to reduce mortality from cardiovascular diseases.

Most of the performance indicators provided for by the programs of demographic and medico-social development of the constituent entities of the Russian Federation are associated with the activities of medical institutions, and the achievement of the planned health indicators really depends on the performance indicators of healthcare institutions.

The provision of health authorities at the federal level with sound scientific and methodological advice on the use of tools to measure the overall morbidity/mortality ratio helps to create conditions that motivate the achievement of target (expected) results expressed in terms of public health. Analysis of the ratio of indicators of overall morbidity and mortality from cardiovascular diseases allows timely finding sufficient resources (material, technical, personnel and financial) in those areas that contribute to the achievement of the best efficiency of medical institutions.

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