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

Abstract

Background 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 malaria incidence. The study investigated the effect of environmental covariates on 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. Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. Results Overall, the median (range) monthly temperature was 30 °C (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). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42–2.83) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 8.16 (95% CI: 3.41–20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01–1.52) and the increased cumulative IRR 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. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2–4, with the highest cumulative IRR of 1.57(95% CI: 1.09–2.25) at lag-month 4. Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

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

“…Relevant studies have suggested that with the changes of meteorological factors over the past few years, the suitability of meteorological factors for infectious diseases (e.g., dengue fever, malaria and pathogenic Vibrio) has increased (6). In addition, extreme meteorological factors (e.g., extreme temperature, extreme humidity, and extreme wind speed) significantly affected various infectious diseases (7)(8)(9)(10). As a respiratory infectious disease, PTB exhibits seasonal characteristics (11), primarily due to meteorological factors (12,13).…”

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

“…As such, models did not aim to maximize precision in the specification of the temperature-malaria association, but rather assess the extent to which this generalized association is sensitive to effect modification. Lags were created for both ambient temperature and precipitation, both important predictors of malaria risk [ 3 ], out to six months prior to admission date under the a priori assumption that a biologically plausible time lag for malaria would not extend past four months, and would not be less than one month [ 29 – 34 ]. A combined 12 and 13 week lag—the time between admission date and temperature preceding that date by 77–91 days—in mean weekly temperature was identified as having the most significant and strongest association with malaria hospital admission rates and was thus chosen for further analysis (Table 2 ).…”

Approaches to defining health facility catchment areas in sub-Saharan Africa

“…The authors justified the approach given the lack of updated data, especially healthcareseeking behaviour data. Straight-line distances are unrealistic, do not account for topography, transport modes, the likelihood of living beyond the threshold, lack of updated spatial and healthcareseeking behaviour data, the inability of sick people to walk, facilities are not uniformly attractive, seasonal mobility of people, bypassing of the nearest facility, the catchment is not a function of distance only Radial buffers accounting for geographical barriers [55], enumeration [56,57] or parish boundaries and road networks [34,58] Thiessen polygon, a region incorporating all points that are closer to a given facility than any other [6,[59][60][61] All points that are closer to a given facility than any other Health facility, coarse residential location Straight-line distances are unrealistic, bypassing the nearest facility, does not account for transport modes, healthcare-seeking behaviour and other factors beyond distance, percapita utilization rate is constant within the HFCA Thiessen polygon with boundaries, travel factors, buffers, and population [62] Modelled travel time or distance based on a leastcost path model [5,13,38,[63][64][65][66][67][68][69][70][71][72] or on network analysis [73] often adjusted for facility capacity [74], population [75], Thiessen polygon [76], boundary [77,78] is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) 86] or refined with boundaries, disease rates, and population [12,87].…”