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Background The malaria test positivity rate (TPR) is a key indicator for evaluating the effectiveness of malaria interventions. In Nigeria, routine data from January to June 2021 reported consistently high TPRs, ranging from 73 to 82%, while Oyo State reported TPRs of 70% to 74% during the same period. These figures were inconsistent with malaria therapeutic efficacy studies conducted between October 2009 and November 2010, which reported a much lower TPR of 35%. This discrepancy raised concerns about data quality, increased malaria incidence, or inaccuracies in malaria diagnosis. Methods This study assessed the effect of two interventions aimed at improving the accuracy of TPR data using secondary quantitative data from the National District Health Information System (NDHIS) for both Primary Healthcare Facilities (PHFs) and Secondary Health Facilities (SHFs). The interventions included (1) facility-level audits of used malaria Rapid Diagnostic Test (RDT) cassettes archived at 733 PHFs, initiated in September 2021, and (2) a 10-day basic malaria microscopy training (BMMT) for Laboratory Scientists at 17 SHFs, completed in September 2021. Results At PHFs, the RDT positivity rate declined from 71% in October 2021 to 53% in December 2022. A period review from January to September revealed a decrease in TPR from 62 to 53% in 2022, compared to no difference in TPR for the same period in 2021 with an average TPR of 77%. A paired t-test comparing the mean TPR for each period showed a statistically significant decline of 19.56 (t = 18.081, p < 0.01, CI (17.06–22.05). At SHFs, microscopy-based TPR decreased from 40% in October 2021 to 18% in December 2022. A review of January to September 2021 showed a TPR decline from 53 to 50%, while in 2022, TPR decreased from 25 to 18%. A paired t-test revealed a statistically significant decline of 19.33 in mean TPR at SHFs (t = 8.14, p < 0.01, CI 13.86–24.81). Conclusion This study highlights the critical role of auditing used RDT cassettes and recommends scaling up this approach in PHFs. It also underscores the value of basic malaria microscopy training in improving the quality and accuracy of microscopy-based diagnosis. One limitation of this study is the absence of comparative data from other states in Nigeria where the interventions were not implemented.

COVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While, for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision-makers to plan ahead (e.g., medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e., the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospitals and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIMA). The rigorous analytical framework chosen, i.e., the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead of those quantities. The proposed approach would also allow decision-makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy.

Background Malaria remains a major public health challenge in Ghana. However, heterogeneous transmission necessitates localized data for effective subnational targeting of control measures. The Ho Municipality, characterized by high rainfall and humidity ideal for year-round mosquito breeding, exemplifies a setting where such detailed epidemiological intelligence is needed but currently scarce. This study aimed to bridge this gap by analysing facility-based trends to inform precision public health interventions in this vulnerable region. Methods A retrospective cross-sectional study, performing a census of all available malaria microscopy records from three major healthcare facilities in Ho Municipality over 36 months (January 2020–December 2022) was conducted. Data were extracted from both paper-based logbooks and electronic health records. Descriptive statistics and multivariable regression analyses—specifically, a log-linear model was employed to identify factors associated with parasite density (presented as Geometric Mean Ratios, GMR) and a Poisson regression model to identify factors associated with test positivity (presented as Adjusted Prevalence Ratios, APR). All models were adjusted for age, sex, facility, and year. Results Among 27,171 tests, the overall test positivity rate (TPR) was 8.8%, showing a decline from 9.9% in 2020 to 7.2% in 2022. Significant disparities were observed: school-age children (5–12 years) had the highest TPR (18.0%), and a fourfold disparity existed between Ho Municipal Hospital (21.0% TPR) and Ho Teaching Hospital (5.2% TPR). Transmission peaked seasonally in August (13.9% TPR).  Plasmodium falciparum  was dominant (79.9% of confirmed cases). School-age children and adolescents demonstrated significantly higher parasite densities than adults (aGMR = 3.69 and aGMR = 3.57, respectively). Regression confirmed school-age children (aPR = 3.26) and adolescents (aPR = 3.49) as the highest-risk groups, with a significant age-sex interaction revealing elderly females were also at markedly increased risk (aPR = 2.15). Conclusion This study identifies persistent, significant disparities in malaria burden linked to specific age groups, sex, and health facilities in Ho Municipality. These findings underline the urgent need for a targeted intervention strategy, including school-based chemoprevention programs, enhanced diagnostic support and staffing for high-burden facilities, and pre-emptive vector control ahead of peak rainfall seasons to accelerate progress towards malaria elimination.

Background Indoor residual spraying (IRS) with Actellic 300 CS was conducted in Lira District between July and August 2016. No formal assessment has been conducted to estimate the effect of spraying with Actellic 300 CS on malaria morbidity in the Ugandan settings. This study assessed malaria morbidity trends before and after IRS with Actellic 300 CS in Lira District in Northern Uganda. Methods The study employed a mixed methods design. Malaria morbidity records from four health facilities were reviewed, focusing on 6 months before and after the IRS intervention. The outcome of interest was malaria morbidity defined as; proportion of outpatient attendance due to total malaria, proportion of outpatient attendance due to confirmed malaria and proportion of malaria case numbers confirmed by microscopy or rapid diagnostic test. Since malaria morbidity was based on count data, an ordinary Poisson regression model was used to obtain percentage point change (pp) in monthly malaria cases before and after IRS. A household survey was also conducted in 159 households to determine IRS coverage and factors associated with spraying. A modified Poisson regression model was fitted to determine factors associated with household spray status. Results The proportion of outpatient attendance due to malaria dropped from 18.7% before spraying to 15.1% after IRS. The proportion of outpatient attendance due to confirmed malaria also dropped from 5.1% before spraying to 4.0% after the IRS intervention. There was a decreasing trend in malaria test positivity rate (TPR) for every unit increase in month after spraying. The decreasing trend in TPR was more prominent 5–6 months after the IRS intervention (Adj. pp = − 0.60, P-value = 0.015; Adj. pp = − 1.19, P-value < 0.001). The IRS coverage was estimated at 89.3%. Households of respondents who were formally employed or owned any form of business were more likely to be unsprayed; (APR = 5.81, CI 2.72–12.68); (APR = 3.84, CI 1.20–12.31), respectively. Conclusion Coverage of IRS with Actellic 300 CS was high and was associated with a significant decline in malaria related morbidity 6 months after spraying.

Background Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. Methods This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. Results A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R 2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R 2 value reduced to 0.38. Conclusions In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.

Background India has launched the malaria elimination initiative in February 2016. Studies suggest that estimates of malaria are useful to rationalize interventions and track their impact. Hence, a national study was launched to estimate burden of malaria in India in 2015. Methods For sampling, all 624 districts of India were grouped in three Annual Parasite Incidence (cases per thousand population) categories, < two (low); two-five (moderate) and > five (high) API. Using probability proportional to size (PPS) method, two districts from each stratum were selected covering randomly 200,000 persons per district. Active surveillance was strengthened with 40 trained workers per study district. Data on malaria cases and deaths was collated from all health care providers i.e. pathological laboratories, private practitioners and hospitals in private and public health sectors and was used for analysis and burden estimation. Results Out of 1215,114 population under surveillance, 198,612 (16.3%) tests were performed and 19,386 (9.7%) malaria cases were detected. The malaria cases estimated in India were 3875,078 (95% confidence interval 3792,018–3958,137) with API of 3.05 (2.99–3.12) including 2789,483 (2740,577–2838,389) Plasmodium falciparum with Annual Falciparum Incidence of 2.2 (2.16–2.24). Out of 8025 deaths investigated, 102 (1.27%) were attributed to malaria. The estimated deaths in India were 29,341 (23,354–35,327) including 19,067 (13,665–24,470) confirmed and 10,274 (7694–12,853) suspected deaths in 2015–2016. Conclusions Estimated malaria incidence was about four folds greater than one million reported by the national programme, but three folds lesser than thirteen million estimated by the World Health Organization (WHO). However, the estimated deaths were 93 folds more than average 313 deaths reported by the national malaria programme in 2015–2016. The 29,341 deaths were comparable with 24,000 deaths in 2015 and 22,786 deaths in 2016 estimated by the WHO for India. These malaria estimates can serve as a benchmark for tracking the success of malaria elimination campaign in India.

Background There is an increasing need for finer spatial resolution data on malaria risk to provide micro-stratification to guide sub-national strategic plans. Here, spatial-statistical techniques are used to exploit routine data to depict sub-national heterogeneities in test positivity rate (TPR) for malaria among patients attending health facilities in Kenya. Methods Routine data from health facilities ( n  = 1804) representing all ages over 24 months (2018–2019) were assembled across 8 counties (62 sub-counties) in Western Kenya. Statistical model-based approaches were used to quantify heterogeneities in TPR and uncertainty at fine spatial resolution adjusting for missingness, population distribution, spatial data structure, month, and type of health facility. Results The overall monthly reporting rate was 78.7% (IQR 75.0–100.0) and public-based health facilities were more likely than private facilities to report ≥ 12 months (OR 5.7, 95% CI 4.3–7.5). There was marked heterogeneity in population-weighted TPR with sub-counties in the north of the lake-endemic region exhibiting the highest rates (exceedance probability > 70% with 90% certainty) where approximately 2.7 million (28.5%) people reside. At micro-level the lowest rates were in 14 sub-counties (exceedance probability < 30% with 90% certainty) where approximately 2.2 million (23.1%) people lived and indoor residual spraying had been conducted since 2017. Conclusion The value of routine health data on TPR can be enhanced when adjusting for underlying population and spatial structures of the data, highlighting small-scale heterogeneities in malaria risk often masked in broad national stratifications. Future research should aim at relating these heterogeneities in TPR with traditional community-level prevalence to improve tailoring malaria control activities at sub-national levels.

Background Community case management of malaria (CCMm) is an equity-focused strategy that complements and extends the reach of health services by providing timely and effective management of malaria to populations with limited access to facility-based healthcare. In Kenya, CCMm involves the use of malaria rapid diagnostic tests (RDT) and treatment of confirmed uncomplicated malaria cases with artemether lumefantrine (AL) by community health volunteers (CHVs). The test positivity rate (TPR) from CCMm reports collected by the Ministry of Health in 2018 was two-fold compared to facility-based reports for the same period. This necessitated the need to evaluate the performance of CHVs in conducting malaria RDTs. Methods The study was conducted in four counties within the malaria-endemic lake zone in Kenya with a malaria prevalence in 2018 of 27%; the national prevalence of malaria was 8%. Multi-stage cluster sampling and random selection were used. Results from 200 malaria RDTs performed by CHVs were compared with test results obtained by experienced medical laboratory technicians (MLT) performing the same test under the same conditions. Blood slides prepared by the MLTs were examined microscopically as a back-up check of the results. A Kappa score was calculated to assess level of agreement. Sensitivity, specificity, and positive and negative predictive values were calculated to determine diagnostic accuracy. Results The median age of CHVs was 46 (IQR: 38, 52) with a range (26–73) years. Females were 72% of the CHVs. Test positivity rates were 42% and 41% for MLTs and CHVs respectively. The kappa score was 0.89, indicating an almost perfect agreement in RDT results between CHVs and MLTs. The overall sensitivity and specificity between the CHVs and MLTs were 95.0% (95% CI 87.7, 98.6) and 94.0% (95% CI 88.0, 97.5), respectively. Conclusion Engaging CHVs to diagnose malaria cases under the CCMm strategy yielded results which compared well with the results of qualified experienced laboratory personnel. CHVs can reliably continue to offer malaria diagnosis using RDTs in the community setting.

Recent studies show that Test Positivity Rate (TPR) gains a better correlation than incidence with the number of hospitalized patients in COVID-19 pandemic. Nevertheless, epidemiologists remain sceptical concerning the widespread use of this metric for surveillance, and indicators based on known cases like incidence rate are still preferred despite the large number of asymptomatic carriers, which remain unknown. Our aim is to compare TPR and incidence rate, to determine which of the two has the best characteristics to predict the trend of hospitalized patients in the COVID-19 pandemic.We perform a retrospective study considering 60 outbreak cases, using global and local data from Italy in different waves of the pandemic, in order to detect peaks in TPR time series, and peaks in incidence rate, finding which of the two indicators has the best ability to anticipate peaks in patients admitted in hospitals.On average, the best TPR-based approach anticipates the incidence rate of about 4.6 days (95 % CI 2.8, 6.4), more precisely the average distance between TPR peaks and hospitalized peaks is 17.6 days (95 % CI 15.0, 20.4) with respect to 13.0 days (95 % CI 10.4, 15.8) obtained for incidence. Moreover, the average difference between TPR and incidence rate increased to more than 6 days in the Delta outbreak during summer 2021, where presumably the percentage of asymptomatic carriers was larger.We conclude that TPR should be used as the primary indicator to enable early intervention, and for predicting hospital admissions in infectious diseases with asymptomatic carriers.

Background Hepatitis B virus (HBV), a global public health threat, is targeted for elimination by 2030. As national HBV prevalence and incidence is lacking for South Africa, our study aimed to provide such data in the public health sector. Methods We analysed laboratory-confirmed HBV data from 2015 to 2019 to determine annual prevalence and incidence rates of HBV infection per 100,000 population, HBsAg and anti-HBc IgM test positivity rates, and HBsAg and anti-HBc IgM testing rates per 100,000 population. Time trend and statistical analyses were performed on HBsAg and anti-HBc IgM test positivity rates. Results The national prevalence rate of HBV infection per 100,000 population increased from 56.14 in 2015 to 67.76 in 2019. Over the five years, the prevalence rate was higher in males than females, highest amongst individuals 25 to 49 years old and highest in Gauteng province. The HBsAg test positivity rate dropped from 9.77% in 2015 to 8.09% in 2019. Over the five years, the HBsAg test positivity rate was higher in males than females, amongst individuals 25 to 49 years old and amongst individuals of Limpopo province. Amongst HBsAg positive children under 5 years old, the majority (65.7%) were less than a year old. HBsAg testing rates per 100,000 population were higher in females under 45 years of age and in males 45 years and above. The national incidence rate of acute HBV infection per 100,000 population dropped from 3.17 in 2015 to 1.69 in 2019. Over the five-year period, incidence rates were similar between males and females, highest amongst individuals 20 to 39 years old and highest in Mpumalanga province. Amongst individuals 20 to 24 years old, there was a substantial decline in the incidence and anti-HBc IgM test positivity rates over time. Anti-HBc IgM testing rates per 100,000 population were higher in females under 40 years of age and in males 40 years and above. Conclusion Critical to hepatitis B elimination is strengthened infant vaccination coverage and interruption of vertical transmission. Transmission of HBV infection in adults may be reduced through heightened awareness of transmission routes and prevention measures.