Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis

Assessing the prevalence, risk factors, and socio-demographic predictors of malaria among pregnant women in the Bono East Region of Ghana: a multicentre hospital-based mixed-method cross-sectional study

BACKGROUND

Malaria is one of the world's most lethal vector-borne diseases, causing significant health burdens in endemic countries. Several studies on the prevalence of malaria among pregnant women in Ghana have been conducted in various parts of the country, yielding evidence pointing to intra- and inter-regional variations. The current study assessed the prevalence, risk factors, and sociodemographic predictors of malaria among pregnant women in the Bono East Region of Ghana.

METHODS

This multicentre hospital-based study employed a mixed-method cross-sectional design. A multistage sampling technique was used to select seven health facilities and recruited 1452 pregnant women who attended ANC at seven selected health facilities. Haematological examination, a structured closed-ended questionnaire, in-depth interviews (IDIs), and focus group discussions (FGDs) were used to obtain relevant data. Quantitative data were analysed with STATA 14 (StataCorp, College Station, USA). Likewise, the four-step thematic analysis was used to analyse qualitative data. A significant level was set at (p < 0.05) at a 95% confidence interval (CI).

RESULTS

The ages of the pregnant women at enrolment ranged between 17 and 40 years, with a mean (SD) of 28.8 ± 3.73 (95% C.I: 28.63-29.02). The overall prevalence of malaria infection among pregnant women was 10.8% (95% CI: 9.32-12.56). Presence of farm or domestic animals, living close to drainage tunnels, living near overgrown vegetation, not married, not having formal education, living in extended-type households, living in compound-type households, mud and thatch households, mud and iron sheet households, primigravidae, multiparity, first-time pregnant women, second-time, third-time, fourth-time, and fifth-time ANC visits, blood groups A, B, and AB were independent factors or predictors significantly associated with increased risk of malaria.

CONCLUSION

The current study revealed an approximately 10.8% prevalence of malaria among pregnant women. The prevalence revealed, was, however, higher than the national prevalence of 8.6%. The high prevalence of malaria, associated risk factors, and sociodemographic and maternal predictors highlight the need to strengthen screening for malaria, administer treatments, monitor maternal and foetal health, and provide education and counselling.

Potential impact of climatic factors on malaria in Rwanda between 2012 and 2021: a time-series analysis

BACKGROUND

Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks.

METHODS

The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions.

RESULTS

A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk.

CONCLUSION

The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission.

Bayesian spatio-temporal modelling of environmental, climatic, and socio-economic influences on malaria in Central Vietnam

BACKGROUND

Despite the successful efforts in controlling malaria in Vietnam, the disease remains a significant health concern, particularly in Central Vietnam. This study aimed to assess correlations between environmental, climatic, and socio-economic factors in the district with malaria cases.

METHODS

The study was conducted in 15 provinces in Central Vietnam from January 2018 to December 2022. Monthly malaria cases were obtained from the Institute of Malariology, Parasitology, and Entomology Quy Nhon, Vietnam. Environmental, climatic, and socio-economic data were retrieved using a Google Earth Engine script. A multivariable Zero-inflated Poisson regression was undertaken using a Bayesian framework with spatial and spatiotemporal random effects with a conditional autoregressive prior structure. The posterior random effects were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling.

RESULTS

There was a total of 5,985 Plasmodium falciparum and 2,623 Plasmodium vivax cases during the study period. Plasmodium falciparum risk increased by five times (95% credible interval [CrI] 4.37, 6.74) for each 1-unit increase of normalized difference vegetation index (NDVI) without lag and by 8% (95% CrI 7%, 9%) for every 1ºC increase in maximum temperature (TMAX) at a 6-month lag. While a decrease in risk of 1% (95% CrI 0%, 1%) for a 1 mm increase in precipitation with a 6-month lag was observed. A 1-unit increase in NDVI at a 1-month lag was associated with a four-fold increase (95% CrI 2.95, 4.90) in risk of P. vivax. In addition, the risk increased by 6% (95% CrI 5%, 7%) and 3% (95% CrI 1%, 5%) for each 1ºC increase in land surface temperature during daytime with a 6-month lag and TMAX at a 4-month lag, respectively. Spatial analysis showed a higher mean malaria risk of both species in the Central Highlands and southeast parts of Central Vietnam and a lower risk in the northern and north-western areas.

CONCLUSION

Identification of environmental, climatic, and socio-economic risk factors and spatial malaria clusters are crucial for designing adaptive strategies to maximize the impact of limited public health resources toward eliminating malaria in Vietnam.

Influence of environmental, geographic, socio-demographic, and epidemiological factors on presence of malaria at the community level in two continents

The interactions of environmental, geographic, socio-demographic, and epidemiological factors in shaping mosquito-borne disease transmission dynamics are complex and changeable, influencing the abundance and distribution of vectors and the pathogens they transmit. In this study, 27 years of cross-sectional malaria survey data (1990-2017) were used to examine the effects of these factors on Plasmodium falciparum and Plasmodium vivax malaria presence at the community level in Africa and Asia. Monthly long-term, open-source data for each factor were compiled and analyzed using generalized linear models and classification and regression trees. Both temperature and precipitation exhibited unimodal relationships with malaria, with a positive effect up to a point after which a negative effect was observed as temperature and precipitation increased. Overall decline in malaria from 2000 to 2012 was well captured by the models, as was the resurgence after that. The models also indicated higher malaria in regions with lower economic and development indicators. Malaria is driven by a combination of environmental, geographic, socioeconomic, and epidemiological factors, and in this study, we demonstrated two approaches to capturing this complexity of drivers within models. Identifying these key drivers, and describing their associations with malaria, provides key information to inform planning and prevention strategies and interventions to reduce malaria burden.

Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016-2018: a spatial temporal analysis

Background

Temperature, precipitation, relative humidity (RH), and Normalized Different Vegetation Index (NDVI), influence malaria transmission dynamics. However, an understanding of interactions between socioeconomic indicators, environmental factors and malaria incidence can help design interventions to alleviate the high burden of malaria infections on vulnerable populations. Our study thus aimed to investigate the socioeconomic and climatological factors influencing spatial and temporal variability of malaria infections in Mozambique.

Methods

We used monthly malaria cases from 2016 to 2018 at the district level. We developed an hierarchical spatial-temporal model in a Bayesian framework. Monthly malaria cases were assumed to follow a negative binomial distribution. We used integrated nested Laplace approximation (INLA) in R for Bayesian inference and distributed lag nonlinear modeling (DLNM) framework to explore exposure-response relationships between climate variables and risk of malaria infection in Mozambique, while adjusting for socioeconomic factors.

Results

A total of 19,948,295 malaria cases were reported between 2016 and 2018 in Mozambique. Malaria risk increased with higher monthly mean temperatures between 20 and 29°C, at mean temperature of 25°C, the risk of malaria was 3.45 times higher (RR 3.45 [95%CI: 2.37-5.03]). Malaria risk was greatest for NDVI above 0.22. The risk of malaria was 1.34 times higher (1.34 [1.01-1.79]) at monthly RH of 55%. Malaria risk reduced by 26.1%, for total monthly precipitation of 480 mm (0.739 [95%CI: 0.61-0.90]) at lag 2 months, while for lower total monthly precipitation of 10 mm, the risk of malaria was 1.87 times higher (1.87 [1.30-2.69]). After adjusting for climate variables, having lower level of education significantly increased malaria risk (1.034 [1.014-1.054]) and having electricity (0.979 [0.967-0.992]) and sharing toilet facilities (0.957 [0.924-0.991]) significantly reduced malaria risk.

Conclusion

Our current study identified lag patterns and association between climate variables and malaria incidence in Mozambique. Extremes in climate variables were associated with an increased risk of malaria transmission, peaks in transmission were varied. Our findings provide insights for designing early warning, prevention, and control strategies to minimize seasonal malaria surges and associated infections in Mozambique a region where Malaria causes substantial burden from illness and deaths.

Lag-time effects of vaccination on SARS-CoV-2 dynamics in German hospitals and intensive-care units

Background

The Efficacy and effectiveness of vaccination against SARS-CoV-2 have clearly been shown by randomized trials and observational studies. Despite these successes on the individual level, vaccination of the population is essential to relieving hospitals and intensive care units. In this context, understanding the effects of vaccination and its lag-time on the population-level dynamics becomes necessary to adapt the vaccination campaigns and prepare for future pandemics.

Methods

This work applied a quasi-Poisson regression with a distributed lag linear model on German data from a scientific data platform to quantify the effects of vaccination and its lag times on the number of hospital and intensive care patients, adjusting for the influences of non-pharmaceutical interventions and their time trends. We separately evaluated the effects of the first, second and third doses administered in Germany.

Results

The results revealed a decrease in the number of hospital and intensive care patients for high vaccine coverage. The vaccination provides a significant protective effect when at least approximately 40% of people are vaccinated, whatever the dose considered. We also found a time-delayed effect of the vaccination. Indeed, the effect on the number of hospital patients is immediate for the first and second doses while for the third dose about 15 days are necessary to have a strong protective effect. Concerning the effect on the number of intensive care patients, a significant protective response was obtained after a lag time of about 15-20 days for the three doses. However, complex time trends, e.g. due to new variants, which are independent of vaccination make the detection of these findings challenging.

Conclusion

Our results provide additional information about the protective effects of vaccines against SARS-CoV-2; they are in line with previous findings and complement the individual-level evidence of clinical trials. Findings from this work could help public health authorities efficiently direct their actions against SARS-CoV-2 and be well-prepared for future pandemics.

Analysis of the effect of temperature on tuberculosis incidence by distributed lag non-linear model in Kashgar city, China

The aim of this study was to explore the effect of temperature on tuberculosis (TB) incidence using the distributed lag non-linear model (DLNM) from 2017 to 2021 in Kashgar city, the region with higher TB incidence than national levels, and assist public health prevention and control measures. From January 2017 to December 2021, a total of 8730 cases of TB were reported, with the higher incidence of male than that of female. When temperature was below 1 °C, it was significantly correlated with TB incidence compared to the median observed temperature (15 °C) at lag 7, 14, and 21, and lower temperatures showed larger RR (relative risk) values. High temperature produced a protective effect on TB transmission, and higher temperature from 16 to 31 °C has lower RR. In discussion stratified by gender, the maximum RRs were achieved for both male group and female group at - 15 °C with lag 21, reporting 4.28 and 2.02, respectively. At high temperature (higher than 20 °C), the RR value of developing TB for female group was significantly larger than 1. In discussion stratified by age, the maximum RRs were achieved for all age groups (≤ 35, 36-64, ≥ 65) at - 15 °C with lag 21, reporting 3.20, 2.07, and 3.45, respectively. When the temperature was higher than 20 °C, the RR of the 36-64-year-old group and the ≥ 65-year-old group was significantly larger than 1 at lag 21, while significantly smaller than 1 for cumulative RR at lag 21, reporting 0.11, 95% confidence interval (CI) (0.01, 0.83) and 0.06, 95% CI (0.01, 0.44), respectively. In conclusion, low temperature, especially in extreme level, acts as a high-risk factor inducing TB transmission in Kashgar city. Males exhibit a significantly higher RR of developing TB at low temperature than female, as well as the elderly group in contrast to the young or middle-aged groups. High temperature has a protective effect on TB transmission in the total population, but female and middle-aged and elderly groups are also required to be alert to the delayed RR induced by it.

East Africa International Center of Excellence for Malaria Research: Impact on Malaria Policy in Uganda

Malaria is the leading cause of disease burden in sub-Saharan Africa. In 2010, the East Africa International Center of Excellence for Malaria Research, also known as the Program for Resistance, Immunology, Surveillance, and Modeling of Malaria (PRISM), was established to provide a comprehensive approach to malaria surveillance in Uganda. We instituted cohort studies and a robust malaria and entomological surveillance network at selected public health facilities that have provided a platform for monitoring trends in malaria morbidity and mortality, tracking the impact of malaria control interventions (indoor residual spraying of insecticide [IRS], use of long-lasting insecticidal nets [LLINs], and case management with artemisinin-based combination therapies [ACTs]), as well as monitoring of antimalarial drug and insecticide resistance. PRISM studies have informed Uganda's malaria treatment policies, guided selection of LLINs for national distribution campaigns, and revealed widespread pyrethroid resistance, which led to changes in insecticides delivered through IRS. Our continuous engagement and interaction with policy makers at the Ugandan Ministry of Health have enabled PRISM to share evidence, best practices, and lessons learned with key malaria stakeholders, participate in malaria control program reviews, and contribute to malaria policy and national guidelines. Here, we present an overview of interactions between PRISM team members and Ugandan policy makers to demonstrate how PRISM's research has influenced malaria policy and control in Uganda.

Malaria outbreak facilitated by engagement in activities near swamps following increased rainfall and limited preventive measures: Oyam District, Uganda

In April 2019, the District Health Office of Oyam District, Uganda reported an upsurge in malaria cases exceeding expected epidemic thresholds, requiring outbreak response. We investigated the scope of outbreak and identified exposures for transmission to inform control measures. A confirmed case was a positive malaria rapid diagnostic test or malaria microscopy from 1 January-30 June 2019 in a resident or visitor of Acaba Sub-county, Oyam District. We reviewed medical records at health facilities to get case-patients. We conducted entomological and environmental assessments to determine vector density, and identify aquatic Anopheles habitats, conducted a case-control study to determine exposures associated with illness. Of 9,235 case-patients (AR = 33%), females (AR = 38%) were more affected than males (AR = 20%) (p<0.001). Children <18 years were more affected (AR = 37%) than adults (p<0.001). Among 83 case-patients and 83 asymptomatic controls, 65 (78%) case-patients and 33 (40%) controls engaged in activities <500m from a swamp (ORMH = 12, 95%CI 3.6-38); 18 (22%) case-patients and four (5%) controls lived <500m from rice irrigation sites (ORMH = 8.2, 95%CI 1.8-36); and 23 (28%) case-patients and four (5%) controls had water pools <100m from household for 3-5 days after rainfall (ORMH = 7.3, 95%CI 2.2-25). Twenty three (28%) case-patients and four (5%) controls did not sleep under bed nets the previous night (ORMH = 20, 95%CI 2.7-149); 68 (82%) case-patients and 43(52%) controls did not wear long-sleeved clothes during evenings (ORMH = 9.3, 95%CI 2.8-31). Indoor resting vector density was 4.7 female mosquitoes/household/night. All Anopheles aquatic habitats had Anopheles larvae. Weekly rainfall in 2019 was heavier (6.0±7.2mm) than same period in 2018 (1.8±1.8mm) (p = 0.006). This outbreak was facilitated by Anopheles aquatic habitats near homes created by human activities, following increased rainfall compounded by inadequate use of individual preventive measures. We recommended awareness on use of insecticide-treated bed nets, protective clothing, and avoiding creation of Anopheles aquatic habitats.

Effects and Interaction of Meteorological Factors on Pulmonary Tuberculosis in Urumqi, China, 2013–2019

Background

Most existing studies have only investigated the delayed effect of meteorological factors on pulmonary tuberculosis (PTB). However, the effect of extreme climate and the interaction between meteorological factors on PTB has been rarely investigated.

Methods

Newly diagonsed PTB cases and meteorological factors in Urumqi in each week between 2013 and 2019 were collected. The lag-exposure-response relationship between meteorological factors and PTB was analyzed using the distributed lag non-linear model (DLNM). The generalized additive model (GAM) was used to visualize the interaction between meteorological factors. Stratified analysis was used to explore the impact of meteorological factors on PTB in different stratification and RERI, AP and SI were used to quantitatively evaluate the interaction between meteorological factors.

Results

A total of 16,793 newly diagnosed PTB cases were documented in Urumqi, China from 2013 to 2019. The median (interquartile range) temperature, relative humidity, wind speed, and PTB cases were measured as 11.3°C (−5.0–20.5), 57.7% (50.7–64.2), 4.1m/s (3.4–4.7), and 47 (37–56), respectively. The effects of temperature, relative humidity and wind speed on PTB were non-linear, which were found with the “N”-shaped, “L”-shaped, “N”-shaped distribution, respectively. With the median meteorological factor as a reference, extreme low temperature was found to have a protective effect on PTB. However, extreme high temperature, extreme high relative humidity, and extreme high wind speed were found to increase the risk of PTB and peaked at 31.8°C, 83.2%, and 7.6 m/s respectively. According to the existing monitoring data, no obvious interaction between meteorological factors was found, but low temperature and low humidity (RR = 1.149, 95%CI: 1.003–1.315), low temperature and low wind speed (RR = 1.273, 95%CI: 1.146–1.415) were more likely to cause the high incidence of PTB.

Conclusion

Temperature, relative humidity and wind speed were found to play vital roles in PTB incidence with delayed and non-linear effects. Extreme high temperature, extreme high relative humidity, and extreme high wind speed could increase the risk of PTB. Moreover, low temperature and low humidity, low temperature and low wind speed may increase the incidence of PTB.

Do socio-demographic factors modify the effect of weather on malaria in Kanungu District, Uganda?

Background

There is concern in the international community regarding the influence of climate change on weather variables and seasonality that, in part, determine the rates of malaria. This study examined the role of sociodemographic variables in modifying the association between temperature and malaria in Kanungu District (Southwest Uganda).

Methods

Hospital admissions data from Bwindi Community Hospital were combined with meteorological satellite data from 2011 to 2014. Descriptive statistics were used to describe the distribution of malaria admissions by age, sex, and ethnicity (i.e. Bakiga and Indigenous Batwa). To examine how sociodemographic variables modified the association between temperature and malaria admissions, this study used negative binomial regression stratified by age, sex, and ethnicity, and negative binomial regression models that examined interactions between temperature and age, sex, and ethnicity.

Results

Malaria admission incidence was 1.99 times greater among Batwa than Bakiga in hot temperature quartiles compared to cooler temperature quartiles, and that 6–12 year old children had a higher magnitude of association of malaria admissions with temperature compared to the reference category of 0–5 years old (IRR = 2.07 (1.40, 3.07)).

Discussion

Results indicate that socio-demographic variables may modify the association between temperature and malaria. In some cases, such as age, the weather-malaria association in sub-populations with the highest incidence of malaria in standard models differed from those most sensitive to temperature as found in these stratified models.

Conclusion

The effect modification approach used herein can be used to improve understanding of how changes in weather resulting from climate change might shift social gradients in health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-022-04118-5.