The usefulness of school-based syndromic surveillance for detecting malaria epidemics: experiences from a pilot project in Ethiopia

Analysis of Integrated Information Systems in Community-based and School-based Public Health Surveillance

BACKGROUND: The involvement of the community in supporting health programs requires an integrated information system. Public health registers obtained by the community means some data is collected repeatedly in different formats or leads to data redundancy. AIM: This research aims to analyze and design an integrated information system model of current community-based and school-based public health surveillance based on a system development life cycle approach. METHODS: Data analysis is carried out using content analysis. RESULTS: The results show that entities involved in the system include health cadres in Posyandu (an integrated health post for maternal and child health), Posbindu (an integrated development post of noncommunicable disease), and school health services. The necessary data include data on vital characteristics, maternal and child health, the risk factors of both communicable and noncommunicable diseases, students’ illness complaints, clean and healthy living behavior, mortality, and environmental health. Information obtained includes the health status of an individual, planning on pregnancy and labor, antenatal care visits, stunting data, immunization status, students’ illness complaints, the number of accidents, larva-free rate, and mortality rate. Information from the system is reported to public health centers, the district health office, and district education office. CONCLUSION: The output of the system is useful to complement the recording and reporting of data from health facilities.

Determinants of Malaria Morbidity Among School-Aged Children Living in East Hararghe Zone, Oromia, Ethiopia: A Community-Based Case–Control Study

Background

Understanding the determinants of malaria morbidity offers helpful insights toward the changing malaria situation, which might lead to the adjustment of malaria program activities. Even though the determinants of malaria morbidity remain unknown, school-aged children were the highest malaria morbidity contributors in the East Hararghe Zone. Therefore, this study aimed to assess the determinants of malaria morbidity among school-aged children in the study area from February 1 to May 31, 2020.

Methods

A case-control study was conducted among school-aged children living in ten randomly selected low, moderate, and high malaria transmission kebeles. Cases were confirmed as positive for malaria, while controls were confirmed as negative for malaria among randomly selected school-aged children. Rapid diagnostic testing (RDT) and blood film (BF) malaria testing methods were used. Multivariable logistic regression was used to identify association between malaria and its determinants.

Results

The determinants of malaria infection were having no formal education (adjusted odds ratio (AOR)=4.91, 95% CI: 1.20–20.17), low family wealth index (AOR=2.50, 95% CI: 1.22–5.12), being from rural residence (AOR=2.34, 95% CI: 1.87–4.12), living near to stagnant water (AOR=2.01, 95% CI: 1.14–3.54), having a maximum of three family members (AOR=0.37, 95% CI: 0.18–0.78), using indoor residual spraying (IRS) (AOR=0.15, 95% CI: 0.08–0.29) and long-lasting insecticide-treated net (LLITN) over the last night (AOR=0.19, 95% CI: 0.10–0.35), and living in the house surrounded by cultivated land(AOR=0.24, 95%CI: 0.10-0.60) compared with their counterparts.

Conclusion

This study revealed that residence, family size, education, wealth index, stagnant water existence, and using LLITN and IRS had significant association with malaria morbidity. Thus, all concerned bodies, including the community should strengthen working on stagnant water elimination around their house to cut the breeding site of the malaria vector mosquito. Moreover, the findings have an important implication for improving interventions targeting the economic status and literacy of the society that may help in the reduction of the risk of malaria in the school-aged children.

Applying the Moving Epidemic Method to Establish the Influenza Epidemic Thresholds and Intensity Levels for Age-Specific Groups in Hubei Province, China

BACKGROUND

School-aged children were reported to act as the main transmitter during influenza epidemic seasons. It is vital to set up an early detection method to help with the vaccination program in such a high-risk population. However, most relative studies only focused on the general population. Our study aims to describe the influenza epidemiology characteristics in Hubei Province and to introduce the moving epidemic method to establish the epidemic thresholds for age-specific groups.

METHODS

We divided the whole population into pre-school, school-aged and adult groups. The virology data from 2010/2011 to 2017/2018 were applied to the moving epidemic method to establish the epidemic thresholds for the general population and age-specific groups for the detection of influenza in 2018/2019. The performances of the model were compared by the cross-validation process.

RESULTS

The epidemic threshold for school-aged children in the 2018/2019 season was 15.42%. The epidemic thresholds for influenza A virus subtypes H1N1 and H3N2 and influenza B were determined as 5.68%, 6.12% and 10.48%, respectively. The median start weeks of the school-aged children were similar to the general population. The cross-validation process showed that the sensitivity of the model established with school-aged children was higher than those established with the other age groups in total influenza, H1N1 and influenza B, while it was only lower than the general population group in H3N2.

CONCLUSIONS

This study proved the feasibility of applying the moving epidemic method in Hubei Province. Additional influenza surveillance and vaccination strategies should be well-organized for school-aged children to reduce the disease burden of influenza in China.

Comparing malaria early detection methods in a declining transmission setting in northwestern Ethiopia

Background

Despite remarkable progress in the reduction of malaria incidence, this disease remains a public health threat to a significant portion of the world’s population. Surveillance, combined with early detection algorithms, can be an effective intervention strategy to inform timely public health responses to potential outbreaks. Our main objective was to compare the potential for detecting malaria outbreaks by selected event detection methods.

Methods

We used historical surveillance data with weekly counts of confirmed Plasmodium falciparum (including mixed) cases from the Amhara region of Ethiopia, where there was a resurgence of malaria in 2019 following several years of declining cases. We evaluated three methods for early detection of the 2019 malaria events: 1) the Centers for Disease Prevention and Control (CDC) Early Aberration Reporting System (EARS), 2) methods based on weekly statistical thresholds, including the WHO and Cullen methods, and 3) the Farrington methods.

Results

All of the methods evaluated performed better than a naïve random alarm generator. We also found distinct trade-offs between the percent of events detected and the percent of true positive alarms. CDC EARS and weekly statistical threshold methods had high event sensitivities (80–100% CDC; 57–100% weekly statistical) and low to moderate alarm specificities (25–40% CDC; 16–61% weekly statistical). Farrington variants had a wide range of scores (20–100% sensitivities; 16–100% specificities) and could achieve various balances between sensitivity and specificity.

Conclusions

Of the methods tested, we found that the Farrington improved method was most effective at maximizing both the percent of events detected and true positive alarms for our dataset (> 70% sensitivity and > 70% specificity). This method uses statistical models to establish thresholds while controlling for seasonality and multi-year trends, and we suggest that it and other model-based approaches should be considered more broadly for malaria early detection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10850-5.

Forecasting influenza-like illness trends in Cameroon using Google Search Data

Although acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied and compared a range of robust statistical and machine learning models including random forest (RF) regression, support vector machines (SVM) regression, multivariable linear regression and ARIMA models to forecast 2012 to 2018 trends of reported ILI cases in Cameroon, using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The R2 and RMSE (Root Mean Squared Error) were statistically similar across most of the methods, however, RF and SVM had the highest average R2 (0.78 and 0.88, respectively) for predicting ILI per 100,000 persons at the country level. This study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and the usefulness of search data for monitoring ILI in sub-Saharan African countries.

The relationship between facility-based malaria test positivity rate and community-based parasite prevalence

INTRODUCTION

Malaria surveillance is a key pillar in the control of malaria in Africa. The value of using routinely collected data from health facilities to define malaria risk at community levels remains poorly defined.

METHODS

Four cross-sectional parasite prevalence surveys were undertaken among residents at 36 enumeration zones in Kilifi county on the Kenyan coast and temporally and spatially matched to fever surveillance at 6 health facilities serving the same communities over 12 months. The age-structured functional form of the relationship between test positivity rate (TPR) and community-based parasite prevalence (PR) was explored through the development of regression models fitted by alternating the linear, exponential and polynomial terms for PR. The predictive ranges of TPR were explored for PR endemicity risk groups of control programmatic value using cut-offs of low (PR <5%) and high (PR ≥ 30%) transmission intensity.

RESULTS

Among 28,134 febrile patients encountered for malaria diagnostic testing in the health facilities, 12,143 (43.2%: 95% CI: 42.6%, 43.7%) were positive. The overall community PR was 9.9% (95% CI: 9.2%, 10.7%) among 6,479 participants tested for malaria. The polynomial model was the best fitting model for the data that described the algebraic relationship between TPR and PR. In this setting, a TPR of ≥ 49% in all age groups corresponded to an age-standardized PR of ≥ 30%, while a TPR of < 40% corresponded to an age-standardized PR of < 5%.

CONCLUSION

A non-linear relationship was observed between the relative change in TPR and changes in the PR, which is likely to have important implications for malaria surveillance programs, especially at the extremes of transmission. However, larger, more spatially diverse data series using routinely collected TPR data matched to community-based infection prevalence data are required to explore the more practical implications of using TPR as a replacement for community PR.

Nationwide school malaria parasitaemia survey in public primary schools, the United Republic of Tanzania

Background

A nationwide, school, malaria survey was implemented to assess the risk factors of malaria prevalence and bed net use among primary school children in mainland Tanzania. This allowed the mapping of malaria prevalence at council level and assessment of malaria risk factors among school children.

Methods

A cross-sectional, school, malaria parasitaemia survey was conducted in 25 regions, 166 councils and 357 schools in three phases: (1) August to September 2014; (2) May 2015; and, (3) October 2015. Children were tested for malaria parasites using rapid diagnostic tests and were interviewed about household information, parents’ education, bed net indicators as well as recent history of fever. Multilevel mixed effects logistic regression models were fitted to estimate odds ratios of risk factors for malaria infection and for bed net use while adjusting for school effect.

Results

In total, 49,113 children were interviewed and tested for malaria infection. The overall prevalence of malaria was 21.6%, ranging from < 0.1 to 53% among regions and from 0 to 76.4% among councils. The malaria prevalence was below 5% in 62 of the 166 councils and above 50% in 18 councils and between 5 and 50% in the other councils. The variation of malaria prevalence between schools was greatest in regions with a high mean prevalence, while the variation was marked by a few outlying schools in regions with a low mean prevalence. Overall, 70% of the children reported using mosquito nets, with the highest percentage observed among educated parents (80.7%), low land areas (82.7%) and those living in urban areas (82.2%).

Conclusions

The observed prevalence among school children showed marked variation at regional and sub-regional levels across the country. Findings of this survey are useful for updating the malaria epidemiological profile and for stratification of malaria transmission by region, council and age groups, which is essential for guiding resource allocation, evaluation and prioritization of malaria interventions.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2601-1) contains supplementary material, which is available to authorized users.

Challenges in laboratory diagnosis of acute viral central nervous system infections in the era of emerging infectious diseases: the syndromic approach

INTRODUCTION

Many acute viral infections of the central nervous system (CNS) remain without etiological diagnosis. Specific treatment is available for only few of them; however, accurate diagnosis is essential for patient's life and public health.

AREAS COVERED

In the current article, the main parameters playing a role for a successful etiological diagnosis of acute CNS infections are analysed and the syndromic approach based on clinical and demographic data combined with surrogated indicators is discussed. For the development of a relevant test panel, knowledge on the microbes causing CNS infections in a particular geographic region is essential. The modern screening strategies covering a large panel of potential causative agents are described. Examples of the successful application of next generation sequencing in the identification of etiological agents, including novel and emerging viruses, are given. Expert commentary: Knowledge on epidemiology of the viruses, expertise on syndromic grouping of the etiological agents and advances in technology enable the laboratory diagnosis of acute CNS infections, and the rapid identification, containment and mitigation of probable outbreaks.