Estimating the patterns and consequences of malaria transmission dynamics on fine spatial scales

Plasmodium falciparum is the leading cause of malaria infection and a major cause of morbidity and mortality across the globe, particularly in the African region. The burden of malaria is unevenly distributed, with some countries, districts or even households within villages harboring a disproportionally higher burden. There is an intricate relationship between the mosquito vector, humans and the parasites they carry, and how they interact with the environment. Small movements on a fine-scale lead to the patterns observed in the community. Quantifying transmission dynamics on a fine-scale, how malaria infections spread locally and the processes leading to the observed spatial and temporal distribution patterns is important for many aspects of malaria epidemiology, in particular, the design of targeted interventions against malaria, the design of studies to evaluate the effectiveness of vector control in the field, and the parameterization of mathematical models to predict the likely impact of interventions for settings where data is not available. Mathematical and statistical models have been developed to quantify fine scale malaria transmission dynamics and investigate the effects of interventions. Since data on the spread of vectors and parasites is challenging to collect, it is not available from many endemic settings for analytic methods to provide estimates, or to validate model predictions. Due to variability between settings, findings from one setting cannot be easily generalized. There is thus a need to involve methods that can extract information from imperfect but available datasets, to make the most of the existing data sources from settings with a variety of characteristics. The overall aim of this thesis was to use statistical and mathematical modelling approaches to characterize fine scale malaria transmission dynamics and their consequences on the measurement of heterogeneity on a local scale for targeted interventions. Chapter 2 used an established comprehensive simulator of malaria epidemiology developed at the Swiss Tropical and Public Health Institute (Swiss TPH) to predict the proportion of malaria infections that are in mosquitoes and humans and how this varies by setting specific characteristics. A substantial proportion of infections was predicted to be in mosquitoes, to vary with setting specific characteristics, and in response to interventions. The predictions also highlighted the role of the dynamics of infections in humans and mosquitoes following the introduction or scale-up of interventions. In Chapter 3, a statistical model which takes into account movement between houses in a village to estimate how far and where mosquitoes fly to in the presence of spatial repellents was developed. This was a secondary use of data on mosquito densities. The method evaluation using simulation showed that the model could be used as a potential tool to gain information on mosquito movement, estimating the distance between the houses the mosquitoes were repelled from and the houses they move to, the proportion of mosquitoes repelled, and the proportion of repelled mosquitoes moving to another house as opposed to somewhere outside. However, the trial data needs to contain sufficient information to be able to disentangle the effects of the underlying processes and provide accurate estimates for all the parameters. We found that additional data on the total number of mosquitoes and sufficient numbers of mosquitoes repelled were required in the case of the motivating trial. Findings from the simulations could inform the design of studies and help quantify criteria for trial settings. In Chapter 4, a simulation method was developed and applied to data on parasite genotypes from Kilifi County, Kenya. A previous study found an interaction between time and geographical distance on the genetic difference between pairs of parasite genotypes: genetic differences were lower for pairs of parasite genotypes which were evaluated within a shorter time interval and found within a shorter geographic distance apart. A stochastic individual-based model of malaria infections, people and homesteads was developed and fitted to the genetic differences in order to investigate hypotheses and parameter values consistent with the observed interaction. The observed interaction could be reproduced by the individual-based model. Although hypothesis about immunity to previously seen genotypes, and or a limit on the number of current infections per individual could not be ruled out, they were not necessary to account for the observed interaction. The mean geographical distance between parent and offspring infections was estimated to be 0.40km (95%CI 0.24 – 1.20), in the base model. This was the first modeling study that we know of which has attempted to estimate parameter values and test hypotheses from malaria genotyping data with a low coverage of infections in a setting with moderate transmission. The findings glean some insights on how simulation can be used in quantifying factors driving transmission, and in estimating unknown parameters when analytic methods are limited. The work in Chapter 5 uses the simulation model developed in Chapter 4 to investigate how the method chosen, local seasonality and movement of infections influence the detection of areas of higher transmission on fine spatial scales for targeted interventions. Our findings show that the identification of hotspots was less accurate when there was a gentle decay in risk from the hotspot boundary, the hotspot was irregularly shaped, there was seasonality in the area or when the mean distance between parent and offspring infections was longer. The findings highlight the importance of setting characteristics, the choice of outcome, and method of detection on the accuracy of identifying areas of higher transmission for targeted interventions. The underlying fine scale transmission dynamics should be taken into account when performing and interpreting analyses of heterogeneity for targeted interventions. Taken as a whole, this thesis provides information on the characteristics of transmission dynamics on a fine scale. It highlights that a substantial proportion of malaria infections are in mosquitoes, and places emphasis on the role that vectors, and humans play in the spread of infections and the implications of fine scale movement for the measurement of heterogeneity for targeted interventions. The estimates have implications for the design and evaluation of malaria control and elimination interventions

Can trials of spatial repellents be used to estimate mosquito movement?

Knowledge of mosquito movement would aid the design of effective intervention strategies against malaria. However, data on mosquito movement through mark-recapture or genetics studies are challenging to collect, and so are not available for many sites. An additional source of information may come from secondary analyses of data from trials of repellents where household mosquito densities are collected. Using the study design of published trials, we developed a statistical model which can be used to estimate the movement between houses for mosquitoes displaced by a spatial repellent. The method uses information on the different distributions of mosquitoes between houses when no households are using spatial repellents compared to when there is incomplete coverage. The parameters to be estimated are the proportion of mosquitoes repelled, the proportion of those repelled that go to another house and the mean distance of movement between houses. Estimation is by maximum likelihood.; We evaluated the method using simulation and found that data on the seasonal pattern of mosquito densities were required, which could be additionally collected during a trial. The method was able to provide accurate estimates from simulated data, except when the setting has few mosquitoes overall, few repelled, or the coverage with spatial repellent is low. The trial that motivated our analysis was found to have too few mosquitoes caught and repelled for our method to provide accurate results.; We propose that the method could be used as a secondary analysis of trial data to gain estimates of mosquito movement in the presence of repellents for trials with sufficient numbers of mosquitoes caught and repelled and with coverage levels which allow sufficient numbers of houses with and without repellent. Estimates from this method may supplement those from mark-release-recapture studies, and be used in designing effective malaria intervention strategies, parameterizing mathematical models and in designing trials of vector control interventions

Efficacy of text-message reminders on paediatric malaria treatment adherence and their post-treatment return to health facilities in Kenya: a randomized controlled trial

BACKGROUND: Short Message Service (SMS) reminders have been suggested as a potential intervention for improving adherence to medications and health facility attendance. METHODS: An open-label, randomized, controlled trial to test the efficacy of automated SMS reminders in improving adherence to artemether-lumefantrine (AL) and post-treatment attendance in comparison with standard care was conducted at four health facilities in western Kenya. Children below five years of age with uncomplicated malaria were randomized to intervention (SMS reminders) or control groups. Within each study group they were further randomized to three categories, which determined the timing of home visits to measure adherence to complete AL course and to individual AL doses. A sub-set of caregivers was advised to return to the facility on day 3 and all were advised to return after 28 days. The primary outcomes were adherence to medication and return on day 3. The primary analysis was by intention-to-treat. RESULTS: Between 9 June, 2014 and 26 February, 2016, 1677 children were enrolled. Of 562 children visited at home on day 3, all AL doses were completed for 97.6% (282/289) of children in the control and 97.8% (267/273) in the intervention group (OR = 1.10; 95% CI = 0.37-3.33; p = 0.860). When correct timing in taking each dose was considered a criteria for adherence, 72.3% (209/289) were adherent in the control and 69.2% (189/273) in the intervention group (OR = 0.82; 95% CI = 0.56-1.19; p = 0.302). Sending SMS reminders significantly increased odds of children returning to the facility on day 3 (81.4 vs 74.0%; OR = 1.55; 95% CI = 1.15-2.08; p = 0.004) and on day 28 (63.4 vs 52.5%; OR = 1.58; 95% CI = 1.30-1.92; p < 0.001). CONCLUSIONS: In this efficacy trial, SMS reminders increased post-treatment return to the health facility, but had no effect on AL adherence which was high in both control and intervention groups. Further effectiveness studies under the real world conditions are needed to determine the optimum role of SMS reminders. Trial registration ISRCTN39512726

Completeness of malaria indicator data reporting via the District Health Information Software 2 in Kenya, 2011–2015

Abstract Background Health facility-based data reported through routine health information systems form the primary data source for programmatic monitoring and evaluation in most developing countries. The adoption of District Health Information Software (DHIS2) has contributed to improved availability of routine health facility-based data in many low-income countries. An assessment of malaria indicators data reported by health facilities in Kenya during the first 5 years of implementation of DHIS2, from January 2011 to December 2015, was conducted. Methods Data on 19 malaria indicators reported monthly by health facilities were extracted from the online Kenya DHIS2 database. Completeness of reporting was analysed for each of the 19 malaria indicators and expressed as the percentage of data values actually reported over the expected number; all health facilities were expected to report data for each indicator for all 12 months in a year. Results Malaria indicators data were analysed for 6235 public and 3143 private health facilities. Between 2011 and 2015, completeness of reporting in the public sector increased significantly for confirmed malaria cases across all age categories (26.5–41.9%, p < 0.0001, in children aged <5 years; 30.6–51.4%, p < 0.0001, in persons aged ≥5 years). Completeness of reporting of new antenatal care (ANC) clients increased from 53.7 to 70.5%, p < 0.0001). Completeness of reporting of intermittent preventive treatment in pregnancy (IPTp) decreased from 64.8 to 53.7%, p < 0.0001 for dose 1 and from 64.6 to 53.4%, p < 0.0001 for dose 2. Data on malaria tests performed and test results were not available in DHIS2 from 2011 to 2014. In 2015, sparse data on microscopy (11.5% for children aged <5 years; 11.8% for persons aged ≥5 years) and malaria rapid diagnostic tests (RDTs) (8.1% for all ages) were reported. In the private sector, completeness of reporting increased significantly for confirmed malaria cases across all age categories (16.7–23.1%, p < 0.0001, in children aged <5 years; 19.4–28.6%, p < 0.0001, in persons aged ≥5 years). Completeness of reporting also improved for new ANC clients (16.2–23.6%, p < 0.0001), and for IPTp doses 1 and 2 (16.6–20.2%, p < 0.0001 and 15.5–20.5%, p < 0.0001, respectively). In 2015, less than 3% of data values for malaria tests performed were reported in DHIS2 from the private sector. Conclusions There have been sustained improvements in the completeness of data reported for most key malaria indicators since the adoption of DHIS2 in Kenya in 2011. However, major data gaps were identified for the malaria-test indicator and overall low reporting across all indicators from private health facilities. A package of proven DHIS2 implementation interventions and performance-based incentives should be considered to improve private-sector data reporting

Algunos aspectos del mundo funerario maya de los siglos XVI y XVII a través de las crónicas y la cultura material

El mundo funerario refleja muchos de los valores que definen a una cultura. Cuando se trata de un modelo único de tradición autóctona, los cambios en la concepción ideológica y su materialización no suelen ser de gran significación pese a las imposiciones de pueblos vecinos, con los que comparten similares raíces culturales. Sin embargo, cuando es un modelo en el que dos culturas de tradición diametralmente diferentes entran en relación y/o conflicto, los cambios son mucho más drásticos y afectan todas las vertientes de la vida cotidiana. El presente estudio analiza la realidad funeraria rural maya durante los siglos XVI y XVII a partir de las fuentes escritas y los materiales arqueológicos de las excavaciones efectuadas. En muchos casos, a pesar de responder al modelo católico de enterrar, por su localización y disposición del cuerpo, ciertos aspectos retrotraen hacia un pasado y unas raíces que nada tienen que ver con los valores del nuevo orden político. En este sentido, la documentación escrita hace poca o nula mención a este importante aspecto, mientras que en el registro arqueológico de los diferentes modelos estudiados sí se encuentran casos diversos

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BackgroundEstimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period.Methods22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.FindingsGlobal all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.InterpretationGlobal adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic