|Year : 2015 | Volume
| Issue : 2 | Page : 215-220
An analysis of underlying factors for seasonal variation in gonorrhoea in India: A 6-year statistical assessment
M Kakran, M Bala, V Singh
Apex Regional STD Teaching, Training and Research Centre, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
|Date of Submission||12-Feb-2014|
|Date of Acceptance||22-Sep-2014|
|Date of Web Publication||10-Apr-2015|
Apex Regional STD Teaching, Training and Research Centre, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi
Source of Support: None, Conflict of Interest: None
Purpose: A statistical assessment of a disease is often necessary before resources can be allocated to any control programme. No literature on seasonal trends of gonorrhoea is available from India. Objectives: The objectives were (1) to determine, if any, seasonal trends were present in India (2) to describe factors contributing to seasonality of gonorrhoea (3) to formulate approaches for gonorrhoea control at the national level. Materials and Methods: Seasonal indices for gonorrhoea were calculated quarterly in terms of a seasonal index between 2005 and 2010. Ratio-to-moving average method was used to determine the seasonal variation. The original data values in the time-series were expressed as percentages of moving averages. Results were also analyzed by second statistical method i.e. seasonal subseries plot. Results: The seasonally adjusted average for culture-positive gonorrhoea cases was highest in the second quarter (128.61%) followed by third quarter (108.48%) while a trough was observed in the first (96.05%) and last quarter (64.85%). The second quarter peak was representative of summer vacations in schools and colleges. Moreover, April is the harvesting month followed by celebrations and social gatherings. Both these factors are associated with increased sexual activity and partner change. A trough in first and last quarter was indicative of festival season and winter leading to less patients reporting to the hospital. Conclusion: The findings highlight the immediate need to strengthen sexual health education among young people in schools and colleges and education on risk-reduction practices especially at crucial points in the calendar year for effective gonorrhoea control.
Keywords: Gonorrhoea, ratio-to-moving average method, seasonal variation, seasonal subseries plot, sexual health education
|How to cite this article:|
Kakran M, Bala M, Singh V. An analysis of underlying factors for seasonal variation in gonorrhoea in India: A 6-year statistical assessment. Indian J Med Microbiol 2015;33:215-20
|How to cite this URL:|
Kakran M, Bala M, Singh V. An analysis of underlying factors for seasonal variation in gonorrhoea in India: A 6-year statistical assessment. Indian J Med Microbiol [serial online] 2015 [cited 2020 Jun 1];33:215-20. Available from: http://www.ijmm.org/text.asp?2015/33/2/215/154853
| ~ Introduction|| |
Seasonal variation is a well-known phenomenon in life and health sciences. Systematic recurrence of sequences forms a seasonal pattern typical of a specific pathogen in a given population and in a given locality.  Seasonal variation in human behaviour has a major effect on diseases incidence.  It is a component of time series which is defined as the repetitive and predictable movement around the trend line. Every disease occurs at any season of the year but some of them more frequently occur and are of greater severity at certain times (Hippocrates. Aphorisms, III, 19). A seasonal pattern may appear as a tight cluster of isolated outbreaks that occurred during a relatively short time period, then spreading over a wide geographic area.  The use of the term seasonality comes from fact that the seasons exert an unquestionable influence on economic and social activity.  Seasonal infections of humans range from childhood diseases, such as measles, diphtheria and chickenpox; to faecal-oral infections, such as cholera and rotavirus; vector-borne diseases including malaria; and even sexually transmitted gonorrhea. ,
Gonorrhoea is one of the common sexually transmitted diseases producing considerable morbidity throughout the world.  The epidemiology of gonococcal infection is complex with many different factors influencing the incidence rates in different geographical areas and within defined areas over time. Earlier published studies on the seasonality of sexually transmitted diseases (STDs) have focussed on the cyclic variation and time trends in the incidence of chlamydia. ,,, Seasonality of gonorrhoea was reported first in the United States in 1971  and then from 1999 to 2003.  It was also observed in Austria, Sweden, Bulgaria in 1977,  Scotland during 1972 to 1976 and 1984 to 1989, , Africa between 1978 and 1983  and in Israel from 1978 to 2008. 
Many factors have been proposed to explain seasonality of STDs in the above mentioned studies. However, no single theory has proved satisfactory explanations about their cause abound thus, they have been variously attributed to changes in atmospheric conditions, the prevalence or virulence of the pathogen, or changes in infection-control measures or changes in sexual norms and the behaviour of the host or holidays such as Christmas, annual holidays for school or others. , To develop efficient strategies for disease prevention and control, there is a need to evaluate the main determinants of seasonal fluctuations in diseases patterns.
A statistical assessment of a disease is often necessary before resources can be allocated to its control. Various statistical techniques can be used for detecting seasonality. Seasonal variation in gonorrhoea in the United States was measured in terms of an index, called a seasonal index.  Cleveland, Dunn and Terpenning in 1978 introduced cycle plots to study the behaviour of seasonal time series. , A cycle plot shows both the cycle or trend and the day-of-the-week or the month-of-the-year effect. The cycle plot is also known as subseries plot which uses monthly or quarterly data. This plot is only useful if the period of the seasonality is already known. No literature on seasonal trends of gonorrhoea is available from India. We used this subseries plot and seasonal index method to analyze the data quarterly.
The aims of this study were (1) to determine, if any, seasonal trends were present in India between 2005 and 2010 (2) to describe factors contributing to seasonality of gonorrhoea (3) to analyze seasonal variation by two different statistical methods and to observe the difference in seasonal trend by both the methods (4) to formulate approaches for gonorrhoea control at the national level considering the various factors responsible for seasonality in India.
| ~ Materials and Methods|| |
A total of 295 gonorrhoea cases reported positive by both the smear and culture at Apex Regional STD Teaching, Training and Research Centre in New Delhi were included in the study. Standard protocols were used for isolation and identification of Neisseria More Details gonorrhoeae. , Seasonality for gonorrhoea morbidity in this study was evaluated by the following two methods:
Ratio-to-moving average method
Seasonal indices were calculated quarterly by the methods described in standard texts on statistics.  January to March represented the first quarter; April to June, second quarter; July to September, third quarter and October to December, fourth or last quarter. Seasonal variation was measured in terms of an index, called a seasonal index. It is an average that can be used to compare an actual observation relative to what it would be if there was no seasonal variation. An index value was attached to each period of the time series within a year. This implies that there were 4 index values for quarterly data.
Ratio-to-moving average method provided an index to measure the degree of the Seasonal Variation in a time series. The index was based on a mean of 100, with the degree of seasonality measured by variations away from the base. This method is also called the percentage moving average method. In this method, the original data values in the time-series were expressed as percentages of moving averages. The steps and the tabulations to calculate the seasonal index are given below:
- We found the fourquarterly moving averages of the original data values in the time-series
- Each original data value of the time-series was expressed as a percentage of the corresponding centered moving average values obtained in step I. In other words, in a multiplicative time-series model, we get (Original data values)/(Trend values)*100= Trend (T)*Cycle (C)*Seasonal (S)*Irregular (I)/(T*C)*100= (S*I)*100. This implies that the ratio--to--moving average represents the seasonal and irregular components
- The percentages were arranged according to quarter of given years and the averages over all the quarters of the given years were calculated.
- If the sum of these indices was not 400 for quarterly figures, it was then multiplied by a correction factor =400/(sum of quarterly indices).
Seasonal subseries plot
Seasonal subseries plot, a specialized graphical technique for detecting seasonality in a time series was also used to detect the seasonality. Excel menus were used to make a cycle plot. The different functions of excel were used like sum of, average, offset, trend offset to calculate and plot average and trend for different quarters.  A graph was plotted using the instructions for drawing these plot types to show quarterly seasonal patterns.
| ~ Results|| |
The calculations for gonorrhoea culture-positive cases for four quarterly moving averages and ratio-to-moving averages are shown in [Table 1]. [Table 2] shows overall seasonal indices after seasonal adjustment of the data from the year 2005 to 2010. A peak was observed in the reported cases in the second quarter during all the years except in 2006 and it exceeded the annual quarterly mean of cases as represented by the ratio-to-moving average method. A decrease in cases always occurred in the last quarter of each year except in 2008.
|Table 1: Gonorrhoea culture - positive cases and calculations for four quarterly moving averages and ratio - to - moving averages |
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Seasonally adjusted average for gonorrhoea cases was maximum in the second quarter (128.61%) followed by third quarter (108.48%) while a trough was observed in the first and last quarter i.e. about 96.05% and 64.85%, respectively.
[Figure 1] shows the seasonal trend of gonorrhoea cases in the subseries plot. Similar trends in seasonality were observed with the seasonal subseries plot as with the ratio-to-moving average method i.e. a peak in the second quarter and a decline in the last quarter. Average of the cases was highest (15.67) in the second quarter and it was lowest (7.67) in the last quarter.
|Figure 1: Seasonal subseries plot demonstrating the trend during the four quarters. First plot shows the data for the first quarter of every year, the second plot for the second quarter of all 6 years and so on. The horizontal lines represent the means for each quarter of the year|
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| ~ Discussion|| |
A better understanding of seasonal trends is likely to result in better implementation of the optimal control strategies. This is the first study on analysis of seasonal variation in gonorrhoea in India. Most studies of the seasonality of STDs from other countries were conducted some 30 years ago. Sexual norms and behaviour have become much more relaxed and permissive, and the proportion of sexually active unmarried young persons has increased since then. ,,, Thus, it is plausible that the nature of the temporal pattern in the occurrence of STDs during the year today might be different from what was reported several decades ago, when the social gathering of young males and females in the spring and summer was less extensive and when sexual behaviour was less permissive.  First study relating to seasonal variations in the incidence of gonorrhoea was in Britain and United States in the 1970's describing a peak in the third quarter of the year and a trough in the first quarter and fourth quarter. , Seasonal trends were later observed between 1984 and 1989 in Scotland and in the lothian region reporting a peak in the first and third quarter of the year for Scotland but no regular trends in Lothian region.  We observed a change in the previously reported pattern with clear peaks of gonorrhoea cases in the second quarter of the year while a trough was observed in the first and last quarter.
Many explanations have been formulated for seasonality till now. There may be a single factor or a combination of factors may also contribute. These factors include frequency of promiscuous sexual behaviour is seasonal, frequency of reporting to medical facilities by patients varies seasonally, susceptibility to gonorrhoea is seasonal, gonorrhoea virulence is seasonal or, reporting of data is seasonal.  The most likely explanation for the observed trends in the Scotland study was changes in sexual behaviour related to summer vacations and seasonal work patterns. An increased rate of partner change during the summer vacation was likely to explain the peaks in gonorrhoea incidence during August to October in the United States.  Our study points to seasonality with the second quarter peak (April to June) which was representative of the summer vacations in schools and colleges. Moreover, April is the harvesting month in India followed by celebrations and social gatherings for the farmers and migrant population. Both the above factors are associated with increased sexual promiscuity and casual sexual relationships. Similarly, peak incidence from January to May in Burkina Faso, Africa, was correlated with the harvest season and it reflected increased sexual contacts among young, mobile men.  The second quarter peak in the present study might also be due to the increased attendance of patients to the hospital in the holidays. A trough was observed in the first and last quarter of the every year. October to November are the festival months in India and December to February are winter months. The likely explanation for the trough may be that patient attendance to the hospital decreases because of festival season and winter during these months and thus, less number of cases.
A study from Center for Disease Control and Prevention (CDC) examined the seasonal variation and time trends in the incidence of gonorrhoea, chlamydia and syphilis during 1999 to 2003.  Significant three-month cycles were documented in all the three STDs, with four prominent peaks evident in March, May, August, and November. The March and May peaks could be associated with the sexual activities of young adults during spring break for different colleges and universities, when huge numbers of sexually active youth congregate at beach resort settings where there are opportunities for casual sex. , Spring break is also popular in other countries, for example, Great Britain.  The role of the increasingly popular Valentine's Day celebration of love in February, and the increased sexual activity that is presumed to be linked to it, might have been a contributory factor to the March peak in each of the STDs. In CDC study, they proposed that the August peak was representative of the social phenomenon of summer vacation with increased sexual activity during the summer vacation months of June through August, perhaps in part due to inadequate adult supervision.
Gonorrhoea was more often detected in the late summer/early fall (August to October) and least so in the late winter/early spring (March) in an analysis of the City of Houston, Texas Health Department database from 1970 to 1979.  It appeared to be more prevalent in the warmer months in Israel from 1978 to 2008.  The STDs (chlamydia, gonorrhoea and syphilis) in military personnel, based on 1997 and 1998 data, were most commonly diagnosed in the late autumn/early winter (September to January) and least so late in spring.  The findings of a recent American Medical Association (2006) survey conducted on female college students and graduates 17-35 years of age provides a clear and representative picture of the behaviours of spring breakers and their potential risk of acquiring STDs. 
Seasonal variation may be detected by measuring the quantity for small time intervals, such as days, weeks, months or quarters. The seasonal indices in this study were calculated on over the four quarters in a year on the basis of moving average of the quarters. Results were analyzed also on the basis of seasonal subseries plot. In the United States, seasonal indices for gonorrhoea morbidity were calculated quarterly on the basis of moving average method and the peak season for gonorrhoea infections was the July to September quarter.  In CDC study, linear regression was used to ascertain three-month (quarterly) cycles, and a linear mixed auto-regression model was applied to determine the statistical significance of the major peaks relative to the annualized time series mean.  In Scotland study, seasonal variation was analyzed quarterly in 1970 and also from 1984 through 1989.  In Africa study, a single-period moving average was used for monthly analysis of seasonal variations.  In comparison to all the other studies cited for seasonality, two methods of statistical analysis were used in the present study and a clear peak in gonorrhoea cases in the second quarter and a trough in the last quarter was observed with both the methods.
Wellings et al. noting the prevalent seasonal variation in risky sexual behaviours, suggested the need to schedule pre-emptive school sexual health promotion programmes prior to the times of high risk, in the spring, summer and fall. 
The present study has the inherent limitations of a retrospective study design. The data reported in this study are not entirely representative. A major limitation of this and all other such studies pertaining to the epidemiology of STDs is the sole reliance upon monthly or quarterly incident data.  Moreover, in this study only culture-positive cases were considered for analysis.
To conclude, two different statistical methods for analysis of seasonal trend demonstrated a clear peak in gonorrhoea cases in the second quarter during the 6-year study period. The potential explanation for second quarter peak fall into three main categories: (1) It was representative of the increased sexual activity during the summer vacation months of April to June. (2) April is the harvesting month in India with increased chances of promiscuous sexual behaviour. (3) It was also due to the fact that health-seeking behaviour increases in the holidays with more number of patients reporting to the health care facilities. A trough in the first and last quarter was an expression of festival season (October and November) and winter (December to February) in India leading to less patients reporting to the hospital.
We conclude, the seasonal trends in gonorrhea were observed in this study. These trends point to an increase in sexual activity and unsafe sex occurring during the summer vacation and after harvesting season. This study determining underlying factors for seasonality in gonorrhoea highlights the fact that seasonally related socio-cultural events increase sexual activities, decrease the likelihood that it will be protected and limit the access to healthcare services. The seasonal patterns of gonorrhoea have implications for provision of sexual health services and for the timing and targeting of sexual health promotional interventions. This suggests the need to schedule more aggressive public health-related preventive strategies such as sexual health promotion activities in schools and colleges and education on risk-reduction practices in the target population especially at crucial points in the calendar year. Moreover, availability of centres delivering these services should be increased for effective gonorrhoea prevention and control at National level.
| ~ Acknowledgment|| |
The authors are thankful to Indian Council of Medical Research (ICMR) and National AIDS Control Organisation (NACO), Ministry of Health and Family Welfare, Government of India, New Delhi and Delhi State AIDS Control Society for financial assistance. We are thankful to the Medical Superintendent, VMMC and Safdarjung Hospital for permitting us to carry out this study. Technical support of Mrs. Leelamma Peter and Mr. Naveen Chandra Joshi is gratefully acknowledged.
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[Table 1], [Table 2]