Assessing urogenital schistosomiasis and female genital schistosomiasis (FGS) among adolescents in Anaocha, Anambra State, Nigeria: implications for ongoing control efforts

Abstract

Background

Urogenital schistosomiasis (UgS) remains a persistent health challenge among adolescents in Anambra State, Nigeria, despite ongoing control efforts. Mass praziquantel treatment programs, initiated in 2013, primarily target primary school-aged children (5–14 years old), leaving adolescents (10–19 years old) enrolled in secondary schools vulnerable to urogenital schistosomiaisis. Additionally, the extent of female genital schistosomiasis (FGS), a neglected gynaecological manifestation of UgS remains unclear.

Methodology

To address these gaps, a cross-sectional study was conducted in Anaocha Local Government Area from February to May 2023. Four hundred and seventy consenting adolescents aged 10–19 years were enrolled. Urinalysis including urine filtration was employed to confirm haematuria and detect urogenital schistosomiasis (UGS) among the participants. For females with heavy infections (≥ 50 eggs/10 ml urine), a gynaecologist performed colposcopy examinations, complemented by acetic acid and Lugol’s iodine staining to assess for female genital schistosomiasis (FGS) lesions or other related reproductive health conditions. Socio-demographic data, including information on potential risk factors, were systematically collected using the Kobo ToolBox software, following gender-sensitive data collection guidelines. Data were analysed using SPSS version 25, incorporating descriptive statistics, multinomial logistic regression, odds ratios, and significance testing.

Results

Among the 470 adolescents (52.8% females, 47.2% males) examined, an overall UgS prevalence of 14.5% was observed, with an average of 5.25 eggs per 10 ml of urine. Females had a slightly higher prevalence (16.1%), and 7.5% had heavy infections. Although gender differences in infection rates were not statistically significant, males had slightly higher odds of infection (OR: 1.332; 95% CI: 0.791–2.244; p-value: 0.280). Adolescents aged 10–14 had the highest prevalence, with significantly increased odds of infection (OR: 1.720; 95% CI: 1.012–2.923; p-value: 0.045). Colposcopy examinations of females with heavy infections revealed FGS lesions and co-infections with Trichomonas vaginalis. Haematuria, though prevalent (24.6%), was not the sole indicator, as those without it faced significantly higher odds of infection (OR: 2.924; 95% CI: 1.731–4.941; p-value: 0.000). Dysuria and genital itching/burning sensation were other UgS and FGS associated symptoms. Direct water contact was associated with higher infection odds (OR: 2.601; 95% CI: 1.007–6.716; p-value: 0.048). Various risk factors were associated with UgS.

Conclusion

The study highlights the need for a comprehensive Urogenital Schistosomiasis (UGS) control strategy that includes secondary school adolescents, emphasizes risk factor management, promotes safe water practices, and raises awareness about UGS and Female Genital Schistosomiasis (FGS) among adolescents, thus improving control efforts and mitigating this health challenge in the region.

Assessing urogenital schistosomiasis and female genital schistosomiasis (FGS) among adolescents in Anaocha, Anambra State, Nigeria: implications for ongoing control efforts

Abstract

Background

Urogenital schistosomiasis (UgS) remains a persistent health challenge among adolescents in Anambra State, Nigeria, despite ongoing control efforts. Mass praziquantel treatment programs, initiated in 2013, primarily target primary school-aged children (5–14 years old), leaving adolescents (10–19 years old) enrolled in secondary schools vulnerable to urogenital schistosomiaisis. Additionally, the extent of female genital schistosomiasis (FGS), a neglected gynaecological manifestation of UgS remains unclear.

Methodology

To address these gaps, a cross-sectional study was conducted in Anaocha Local Government Area from February to May 2023. Four hundred and seventy consenting adolescents aged 10–19 years were enrolled. Urinalysis including urine filtration was employed to confirm haematuria and detect urogenital schistosomiasis (UGS) among the participants. For females with heavy infections (≥ 50 eggs/10 ml urine), a gynaecologist performed colposcopy examinations, complemented by acetic acid and Lugol’s iodine staining to assess for female genital schistosomiasis (FGS) lesions or other related reproductive health conditions. Socio-demographic data, including information on potential risk factors, were systematically collected using the Kobo ToolBox software, following gender-sensitive data collection guidelines. Data were analysed using SPSS version 25, incorporating descriptive statistics, multinomial logistic regression, odds ratios, and significance testing.

Results

Among the 470 adolescents (52.8% females, 47.2% males) examined, an overall UgS prevalence of 14.5% was observed, with an average of 5.25 eggs per 10 ml of urine. Females had a slightly higher prevalence (16.1%), and 7.5% had heavy infections. Although gender differences in infection rates were not statistically significant, males had slightly higher odds of infection (OR: 1.332; 95% CI: 0.791–2.244; p-value: 0.280). Adolescents aged 10–14 had the highest prevalence, with significantly increased odds of infection (OR: 1.720; 95% CI: 1.012–2.923; p-value: 0.045). Colposcopy examinations of females with heavy infections revealed FGS lesions and co-infections with Trichomonas vaginalis. Haematuria, though prevalent (24.6%), was not the sole indicator, as those without it faced significantly higher odds of infection (OR: 2.924; 95% CI: 1.731–4.941; p-value: 0.000). Dysuria and genital itching/burning sensation were other UgS and FGS associated symptoms. Direct water contact was associated with higher infection odds (OR: 2.601; 95% CI: 1.007–6.716; p-value: 0.048). Various risk factors were associated with UgS.

Conclusion

The study highlights the need for a comprehensive Urogenital Schistosomiasis (UGS) control strategy that includes secondary school adolescents, emphasizes risk factor management, promotes safe water practices, and raises awareness about UGS and Female Genital Schistosomiasis (FGS) among adolescents, thus improving control efforts and mitigating this health challenge in the region.

The game of lies by stock investors in social media: a study based on city lockdowns in China

Abstract

The potential hypotheses for finance research based on social media sentiment revolve around the reliability of investor sentiment expressed on social media and the causal relationship between financial markets and this sentiment. The central hypothesis we focus on is derived from the "lie game" played by investors on social media. This study is the first to explore three states of this lie game in the context of the Chinese stock market: the "equilibrium state", the "confusion state", and the "subversion state". Our findings indicate that the "equilibrium" state is the typical state of the lie game, where increased investor sentiment results in more positive market behavior, and higher stock prices lead to increased investor sentiment. We also examine the effect of significant social events, such as the "lockdown in Wuhan" and the "lockdown in Shanghai", on the lie game's outcome. The successful lockdown in Wuhan and the public's opposition to the politicization of COVID-19 reinforced the "equilibrium" state of the game. However, the Shanghai lockdown's failure to promptly halt the spread of COVID-19 led to the intertwining of the economy and COVID-19 in public discourse, shifting the lie game's outcome from an "equilibrium state" to a "subversive state". We emphasize that the "confusion state" and "subversion state" outcomes of the lie game are concerning, and managing public opinion and the externalization of domestic conflicts can help reduce this risk. This study offers a fresh perspective on the traditional issues of investor sentiment reliability and the causal relationship between investor sentiment and stock markets.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record (QAR) Data Analysis

Abstract

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry, it has become imperative to monitor and mitigate these threats to ensure civil aviation safety. The eddy dissipation rate (EDR) has been established as the standard metric for quantifying turbulence in civil aviation. This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder (QAR) data. The detection of atmospheric turbulence is approached as an anomaly detection problem. Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events. Moreover, comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available. In summary, the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data, comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms. This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record (QAR) Data Analysis

Abstract

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry, it has become imperative to monitor and mitigate these threats to ensure civil aviation safety. The eddy dissipation rate (EDR) has been established as the standard metric for quantifying turbulence in civil aviation. This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder (QAR) data. The detection of atmospheric turbulence is approached as an anomaly detection problem. Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events. Moreover, comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available. In summary, the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data, comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms. This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.