Browsing by Author "Namuganga, Jane F."

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    Associations between environmental covariates and malaria incidence in high transmission settings of Uganda: A distributed non-linear lagged ecological analysis
    (Research Square, 2021) Okiring, Jaffer; Routledge, Isobel; Esptein, Adrienne; Namuganga, Jane F.; Kamya, Emmanuel V.; Odei Obeng-Amoako, Gloria; Maiteki-Sebuguzi, Catherine; Rutazaana, Damian; Kalyango, Joan N.; Kamya, Moses R.; Dorsey, Grant; Wesonga, Ronald; Kiwuwa, Steven M.; Nankabirwa, Joaniter I.
    Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in the incidence of symptomatic malaria. The study aim was to investigate the associations between environmental covariates and malaria incidence in high transmission settings of Uganda. Methods This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period (January 2019 - December 2020). Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthy average measures of temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed non-linear lagged model was used to investigate the quantitative relationship between environmental covariates and malaria incidence. Results Overall, the median (range) monthly temperature was 30oC (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). A non-linear relationship between environmental covariates and malaria incidence was observed. An average monthly temperature of 35oC was associated with significant increases in malaria incidence compared to the median observed temperature (30oC) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the cumulative increases in MI significantly at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag month 4. An average monthly rainfall of 200mm was associated with significant increases in malaria incidence compared to the median observed rainfall (133mm) at lag month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the cumulative IRR increases of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag month 4. An average NVDI of 0.72 was associated with significant cumulative increases in IRR of malaria as compared to the median observed NDVI (0.66) at month lag 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag month 4. The rate of increase in cumulative IRR of malaria was highest within lag months 1-2 as compared to lag months 3-4 for all the environmental covariates. Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with cumulative increases in the incidence of malaria, with peak associations occurring after variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.
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    The impact of stopping and starting indoor residual spraying on malaria burden in Uganda
    (Nature communications, 2021) Namuganga, Jane F.; Epstein, Adrienne; Nankabirwa, Joaniter I.; Mpimbaza, Arthur; Kiggundu, Moses; Sserwanga, Asadu; Kapisi, James; Arinaitwe, Emmanuel; Gonahasa, Samuel; Opigo, Jimmy; Ebong, Chris; Staedke, Sarah G.; Shililu, Josephat; Okia, Michael; Rutazaana, Damian; Maiteki-Sebuguzi, Catherine; Belay, Kassahun; Kamya, Moses R.; Dorsey, Grant; Rodriguez-Barraquer, Isabel
    The scale-up of malaria control efforts has led to marked reductions in malaria burden over the past twenty years, but progress has slowed. Implementation of indoor residual spraying (IRS) of insecticide, a proven vector control intervention, has been limited and difficult to sustain partly because questions remain on its added impact over widely accepted interventions such as bed nets. Using data from 14 enhanced surveillance health facilities in Uganda, a country with high bed net coverage yet high malaria burden, we estimate the impact of starting and stopping IRS on changes in malaria incidence. We show that stopping IRS was associated with a 5-fold increase in malaria incidence within 10 months, but reinstating IRS was associated with an over 5-fold decrease within 8 months. In areas where IRS was initiated and sustained, malaria incidence dropped by 85% after year 4. IRS could play a critical role in achieving global malaria targets, particularly in areas where progress has stalled.
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    Malaria hospitalisation in East Africa: age, phenotype and transmission intensity
    (BMC medicine, 2022) Kamau, Alice; Paton, Robert S.; Akech, Samuel; Mpimbaza, Arthur; Khazenzi, Cynthia; Ogero, Morris; Mumo, Eda; Alegana, Victor A.; Agweyu, Ambrose; Mturi, Neema; Mohammed, Shebe; Bigogo, Godfrey; Audi, Allan; Kapisi, James; Sserwanga, Asadu; Namuganga, Jane F.; Kariuki, Simon; Otieno, Nancy A.; Nyawanda, Bryan O.; Olotu, Ally; Salim, Nahya; Athuman, Thabit; Abdulla, Salim; Mohamed, Amina F.; Mtove, George; Reyburn, Hugh; Gupta, Sunetra; Lourenço, José; Bejon, Philip; Snow, Robert W.
    Understanding the age patterns of disease is necessary to target interventions to maximise costeffective impact. New malaria chemoprevention and vaccine initiatives target young children attending routine immunisation services. Here we explore the relationships between age and severity of malaria hospitalisation versus malaria transmission intensity. Methods: Clinical data from 21 surveillance hospitals in East Africa were reviewed. Malaria admissions aged 1 month to 14 years from discrete administrative areas since 2006 were identified. Each site-time period was matched to a model estimated community-based age-corrected parasite prevalence to provide predictions of prevalence in childhood (PfPR2–10). Admission with all-cause malaria, severe malaria anaemia (SMA), respiratory distress (RD) and cerebral malaria (CM) were analysed as means and predicted probabilities from Bayesian generalised mixed models. Results: 52,684 malaria admissions aged 1 month to 14 years were described at 21 hospitals from 49 site-time locations where PfPR2–10 varied from < 1 to 48.7%. Twelve site-time periods were described as low transmission (PfPR2–10 < 5%), five low-moderate transmission (PfPR2–10 5–9%), 20 moderate transmission (PfPR2–10 10–29%) and 12 high transmission (PfPR2–10 ≥ 30%). The majority of malaria admissions were below 5 years of age (69–85%) and rare among children aged 10–14 years (0.7–5.4%) across all transmission settings. The mean age of all-cause malaria hospitalisation was 49.5 months (95% CI 45.1, 55.4) under low transmission compared with 34.1 months (95% CI 30.4, 38.3) at high transmission, with similar trends for each severe malaria phenotype. CM presented among older children at a mean of 48.7 months compared with 39.0 months and 33.7 months for SMA and RD, respectively. In moderate and high transmission settings, 34% and 42% of the children were aged between 2 and 23 months and so within the age range targeted by chemoprevention or vaccines. Conclusions: Targeting chemoprevention or vaccination programmes to areas where community-based parasite prevalence is ≥10% is likely to match the age ranges covered by interventions (e.g. intermittent presumptive treatment in infancy to children aged 2–23 months and current vaccine age eligibility and duration of efficacy) and the age ranges of highest disease burden.
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    Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis
    (Malaria journal, 2021) Okiring, Jaffer; Epstein, Adrienne; Namuganga, Jane F.; Kamya, Victor; Sserwanga, Asadu; Kapisi, James; Ebong, Chris; Kigozi, Simon P.; Mpimbaza, Arthur; Wanzira, Humphrey; Briggs, Jessica; Kamya, Moses R.; Nankabirwa, Joaniter I.; Dorsey, Grant
    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 R2 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 R2 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.