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# Epidemiology for English speaking students / Tasks for Seminars / Seminar 2 Descriptive epidemiology / Algorithms

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Algorithm of the description of long-term morbidity dynamics

The description of morbidity distribution should be started by naming of the chart

1. The even versus uneven morbidity distribution

• As the rule there is no absolutely even distribution of morbidity in nature, certain values fluctuations always exist. However there is no statistical significant difference between these values, indicating relatively even distribution of morbidity.

• Uneven distribution of morbidity: there is a statistically significantly difference between values of morbidity and there are visually registered rises and falls of morbidity. The statistical significant difference between values is calculated by different statistical methods and plotted on a chart by the method of confidence intervals.

1. The regular versus irregular distribution of morbidity

• Irregular distribution of morbidity: it is impossible to reveal any regularity of periods between rises of morbidity.

• Regular distribution of morbidity: when rises of morbidity occur, at regular (approximately regular) intervals. This situation is named periodicity. Depending on the time span, between morbidity rises, cycles are divided into small of 3-5 years, medium - 6-15 years and large - more than 15 years.

1. The tendency of morbidity distribution or the direction of morbidity dynamics.

• There is no tendency i.e. the levels of morbidity fluctuate around the mean value, and the curve is parallel to the axis Х.

• Rise or fall tendency. If the morbidity value of the first year of a tendency line is significantly differs from the level of a morbidity of last year of a tendency line, then the existence of the tendency is evident.

• If there is neither tendency nor fluctuations it morbidity is considered to be stabilized.

1. Name possible (hypothetical) causes of the given distribution of morbidity.

# Algorithm of the description of monthly morbidity dynamics

The description of distribution of morbidity should be started by naming of the chart

1. The morbidity distribution can be even or uneven

• Even morbidity distribution: morbidity levels in individual month don’ t differ significantly and don’t exceed the level of maximum background morbidity level

• Uneven morbidity distribution: there is statistically significant difference in morbidity rates between individual months. There is visually registered either gradual or steep increase (decrease) of morbidity falls below or rises above maximum background value. Having reached maximum (minimum) level in certain month morbidity falls below or rises above maximum background value; while describing it is worth while to confirm the hypothesis by indicating the range of morbidity fluctuation from minimum to maximum level.

1. The term of the beginning of morbidity rise.

The term of morbidity rise beginning can be determined in several ways, for example: to drop a perpendicular on an axis Х from the point of intersection of the maximum level of background morbidity with the curve of morbidity. The beginning of rise is indicated by the month of a year, if necessary - decade, week of month or in days.

1. The term of morbidity rise termination.

Is determined by last month, when the morbidity was above the maximum background morbidity level.

1. Seasonality.

Seasonality is a regular rise of morbidity, registered practically each year, and approximately in the same time. The rise of morbidity may involve one, two or three natural seasons. Seasonality can be winter, autumnal, summer-autumnal, spring- summer - autumnal seasonality and etc.

Seasonality can be revealed on graphically (on the bases of chats’ data) , but only if the morbidity data embrace the whole studied season and are distributed monthly on a standard curve.

If tabulated or the graphic morbidity data characterized only one year, is impossible to say whether the rise is seasonal.

6. Name possible (hypothetical) causes of the given distribution of morbidity.

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