Epidemiological characteristics of gonorrhea and its influential meteorological factors: a 14-year retrospective assessment in China

Bu, Jin; Zhou, Li; Xiao, Xiang; Yue, Qian; Hu, Gang; Jiang, Pei; Jing, Chen; Lin, Yu Hui

doi:10.1007/s11356-021-17823-6

Cited by 3 publications

(7 citation statements)

References 15 publications

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“…Another study conducted in the US by Martinez et al also revealed a higher morbidity rate, but the age trends were consistent with our findings, with the 19-24-year age group having the highest morbidity rate, followed by the 15-19-year age group [ 6 , 7 , 11 - 22 ]. Our findings were also consistent with another published study [ 16 - 19 ].…”

Section: Discussionsupporting

confidence: 94%

Epidemiological Trends and Factors Associated With the Morbidity Rate of Gonorrhea: A CDC-WONDER Database Analysis

Akinboro,

Mmaduabuchi,

Beeko

et al. 2023

Cureus

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Background and objective: Gonorrhea is a prevalent sexually transmitted illness caused by the bacteria Neisseria gonorrhoeae, leading to serious health consequences such as pelvic inflammatory disease (PID), infertility, ectopic pregnancy, and increased susceptibility to HIV infection. Despite advancements in prevention and treatment, gonorrhea remains a significant public health problem in the United States (US) due to its widespread incidence, potential consequences, and the growth of antibiotic resistance. This study investigates the epidemiological trends and morbidity rates of gonorrhea using the Centers for Disease Control and Prevention's (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) database. The aim is to identify temporal patterns, demographic characteristics, and notable changes in gonorrhea epidemiology to inform targeted therapies and interventions. Methods: The CDC WONDER database, which provides extensive national and state-level data on reported causes of death in the United States, was utilized for this study. We examined the developments in gonorrhea morbidity rates over time, identified demographic differences based on age, gender, and race/ethnicity, and analyzed the disease's regional distribution through a systematic analysis of the database. Aggregate data for selected time periods (1996-2014) were summarized using the morbidity rate per 100,000 people and the total number of cases across the years. Results: This database analysis identified a total of 6,454,097 individuals diagnosed with gonorrhea between 1996 and 2014. The calculated total morbidity rate during this period was 115.4 per 100,000 individuals. The highest morbidity rates were observed in the years 1999 (129.2 per 100,000 people), 1998 (129.1 per 100,000 people), and 2001 (126.8 per 100,000 people), respectively. The District of Columbia reported the highest morbidity rate (478.25 per 100,000 people). In males, the overall morbidity rate over the years was reported to be comparable to females (114 per 100,000 people and 116.3 per 100,000 people, respectively). The analysis revealed consistently higher morbidity rates among individuals aged between 19 and 24 years (525.2 per 100,000 people). Moreover, black or African American individuals consistently exhibited higher morbidity rates (506.1 per 100,000 people) compared to white individuals (16.1 per 100,000 people). Conclusion: The analysis of gonorrhea cases between 1996 and 2014 revealed that the highest rates occurred during specific years, with a particular concentration observed in the District of Columbia. Additionally, certain demographic groups, such as individuals aged 19-24 and the black or African American population, consistently exhibited higher morbidity rates compared to others. These findings emphasize the importance of targeted interventions to address the observed temporal patterns and demographic disparities, in order to effectively combat the spread of gonorrhea.

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Section: Discussionsupporting

confidence: 94%

Epidemiological Trends and Factors Associated With the Morbidity Rate of Gonorrhea: A CDC-WONDER Database Analysis

Akinboro,

Mmaduabuchi,

Beeko

et al. 2023

Cureus

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“…Traditional ARIMA models were widely used in TS data analysis and forecasting, but one of the drawbacks is that it requires stationary data and does not fit well for non-linear TS data. Some studies have shown seasonal fluctuations in gonorrhea incidence [ 4 , 7 ], the same pattern was found when we preprocessed the study samples, so a multiplicative seasonal ARIMA (SARIMA) model was needed. The machine learning (ML) theory and technology rapidly develop in the past several years.…”

Section: Introductionmentioning

confidence: 56%

“…The seasonality of gonorrhea was first reported in the United States in 1971, with peaks in the summer and early autumn [ 7 ], and our seasonal diagnosis showed that the rates of gonorrhea had two peaks in the year, on average, in July and December, which is similar to the results of another study [ 16 ]. One study showed the risk of gonorrhea infection was highest at the temperature range of 6–11 °C [ 4 ], as the meteorological factors contribute to less than 20% [ 4 ] of the variation in infection transmission, so another possible reason for the seasonal fluctuations in gonorrhea is the frequency of sexual activity, which is consistent with the changing patterns of sexually transmitted diseases in the population [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

“…In China, gonorrhea has become the second most prevalent STIs, after syphilis. Since the first gonorrhea case was reported in 1977 [ 4 ], it has been prevalent in China for 46 years and become a public health distress that requires attention. The gonorrhea incidence was 9.06 cases per 100,000 population in 2021 [ 5 ], there is an increase of 20.96% compared with the incidence in 2020 (7.49 cases per 100,000 population) [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model

Wang,

Zhang

et al. 2024

BMC Infect Dis

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Background Gonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions. Methods Time series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the training and validation sets of the models. Results The Seasonal components after data decomposition showed an approximate bimodal distribution with a period of 12 months. The three models identified were SARIMA(1,1,1) (2,1,2)12, LSTM with 150 hidden units, and SARIMA-LSTM with 150 hidden units, the SARIMA-LSTM model fitted best in the training and validation sets, for the smallest MAPE, RMSE, and MPE. Conclusions The overall incidence trend of gonorrhea in mainland China has been on the decline since 2004, with some periods exhibiting an upward trend. The incidence of gonorrhea displays a seasonal distribution, typically peaking in July and December each year. The SARIMA model, LSTM model, and SARIMA-LSTM model can all fit the monthly incidence time series data of gonorrhea in mainland China. However, in terms of predictive performance, the SARIMA-LSTM model outperforms the SARIMA and LSTM models, with the LSTM model surpassing the SARIMA model. This suggests that the SARIMA-LSTM model can serve as a preferred tool for time series analysis, providing evidence for the government to predict trends in gonorrhea incidence. The model's predictions indicate that the incidence of gonorrhea in mainland China will remain at a high level in 2024, necessitating that policymakers implement public health measures in advance to prevent the spread of the disease.

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“…The autumn surge in gonorrhea cases may be linked to hormonal influences that promote increased sexual activity. Research has revealed elevated testosterone levels in the autumn, peaking in October, correlating with heightened sexual activity [ 29 , 30 ]. The ITS-ARIMA model is effective in mitigating seasonal effects when modelling gonorrhea incidence.…”

Section: Discussionmentioning

confidence: 99%

Interruption time series analysis using autoregressive integrated moving average model: evaluating the impact of COVID-19 on the epidemic trend of gonorrhea in China

Li,

Liu,

et al. 2023

BMC Public Health

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Background Interrupted time series (ITS) analysis is a growing method for assessing intervention impacts on diseases. However, it remains unstudied how the COVID-19 outbreak impacts gonorrhea. This study aimed to evaluate the effect of COVID-19 on gonorrhea and predict gonorrhea epidemics using the ITS-autoregressive integrated moving average (ARIMA) model. Methods The number of gonorrhea cases reported in China from January 2005 to September 2022 was collected. Statistical descriptions were applied to indicate the overall epidemiological characteristics of the data, and then the ITS-ARIMA was established. Additionally, we compared the forecasting abilities of ITS-ARIMA with Bayesian structural time series (BSTS), and discussed the model selection process, transfer function, check model fitting, and interpretation of results. Result During 2005–2022, the total cases of gonorrhea were 2,165,048, with an annual average incidence rate of 8.99 per 100,000 people. The highest incidence rate was 14.2 per 100,000 people in 2005 and the lowest was 6.9 per 100,000 people in 2012. The optimal model was ARIMA (0,1, (1,3)) (0,1,1)12 (Akaike’s information criterion = 3293.93). When predicting the gonorrhea incidence, the mean absolute percentage error under the ARIMA (16.45%) was smaller than that under the BSTS (22.48%). The study found a 62.4% reduction in gonorrhea during the first-level response, a 46.47% reduction during the second-level response, and an increase of 3.6% during the third-level response. The final model estimated a step change of − 2171 (95% confidence interval [CI] − 3698 to − 644) cases and an impulse change of − 1359 (95% CI − 2381 to − 338) cases. Using the ITS-ARIMA to evaluate the effect of COVID-19 on gonorrhea, the gonorrhea incidence showed a temporary decline before rebounding to pre-COVID-19 levels in China. Conclusion ITS analysis is a valuable tool for gauging intervention effectiveness, providing flexibility in modelling various impacts. The ITS-ARIMA model can adeptly explain potential trends, autocorrelation, and seasonality. Gonorrhea, marked by periodicity and seasonality, exhibited a downward trend under the influence of COVID-19 intervention. The ITS-ARIMA outperformed the BSTS, offering superior predictive capabilities for the gonorrhea incidence trend in China.

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Epidemiological characteristics of gonorrhea and its influential meteorological factors: a 14-year retrospective assessment in China

Cited by 3 publications

References 15 publications

Epidemiological Trends and Factors Associated With the Morbidity Rate of Gonorrhea: A CDC-WONDER Database Analysis

Epidemiological Trends and Factors Associated With the Morbidity Rate of Gonorrhea: A CDC-WONDER Database Analysis

Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model

Interruption time series analysis using autoregressive integrated moving average model: evaluating the impact of COVID-19 on the epidemic trend of gonorrhea in China

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