Using model-based geostatistics for assessing the elimination of trachoma.

BACKGROUND: Trachoma is the commonest infectious cause of blindness worldwide. Efforts are being made to eliminate trachoma as a public health problem globally. However, as prevalence decreases, it becomes more challenging to precisely predict prevalence. We demonstrate how model-based geostatistics (MBG) can be used as a reliable, efficient, and widely applicable tool to assess the elimination status of trachoma. METHODS: We analysed trachoma surveillance data from Brazil, Malawi, and Niger. We developed geostatistical Binomial models to predict trachomatous inflammation-follicular (TF) and trachomatous trichiasis (TT) prevalence. We proposed a general framework to incorporate age and gender in the geostatistical models, whilst accounting for residual spatial and non-spatial variation in prevalence through the use of random effects. We also used predictive probabilities generated by the geostatistical models to quantify the likelihood of having achieved the elimination target in each evaluation unit (EU). RESULTS: TF and TT prevalence varied considerably by country, with Brazil showing the lowest prevalence and Niger the highest. Brazil and Malawi are highly likely to have met the elimination criteria for TF in each EU, but, for some EUs, there was high uncertainty in relation to the elimination of TT according to the model alone. In Niger, the predicted prevalence varied significantly across EUs, with the probability of having achieved the elimination target ranging from values close to 0% to 100%, for both TF and TT. CONCLUSIONS: We demonstrated the wide applicability of MBG for trachoma programmes, using data from different epidemiological settings. Unlike the standard trachoma prevalence survey approach, MBG provides a more statistically rigorous way of quantifying uncertainty around the achievement of elimination prevalence targets, through the use of spatial correlation. In addition to the analysis of existing survey data, MBG also provides an approach to identify areas in which more sampling effort is needed to improve EU classification. We advocate MBG as the new standard method for analysing trachoma survey outputs.

Impact of population based indoor residual spraying with and without mass drug administration with dihydroartemisinin-piperaquine on malaria prevalence in a high transmission setting: a quasi-experimental controlled before-and-after trial in northeastern Uganda.

BACKGROUND: Declines in malaria burden in Uganda have slowed. Modelling predicts that indoor residual spraying (IRS) and mass drug administration (MDA), when co-timed, have synergistic impact. This study investigated additional protective impact of population-based MDA on malaria prevalence, if any, when added to IRS, as compared with IRS alone and with standard of care (SOC). METHODS: The 32-month quasi-experimental controlled before-and-after trial enrolled an open cohort of residents (46,765 individuals, 1st enumeration and 52,133, 4th enumeration) of Katakwi District in northeastern Uganda. Consented participants were assigned to three arms based on residential subcounty at study start: MDA+IRS, IRS, SOC. IRS with pirimiphos methyl and MDA with dihydroartemisinin- piperaquine were delivered in 4 co-timed campaign-style rounds 8 months apart. The primary endpoint was population prevalence of malaria, estimated by 6 cross-sectional surveys, starting at baseline and preceding each subsequent round. RESULTS: Comparing malaria prevalence in MDA+IRS and IRS only arms over all 6 surveys (intention-to-treat analysis), roughly every 6 months post-interventions, a geostatistical model found a significant additional 15.5% (95% confidence interval (CI): [13.7%, 17.5%], Z = 9.6, p = 5e-20) decrease in the adjusted odds ratio (aOR) due to MDA for all ages, a 13.3% reduction in under 5's (95% CI: [10.5%, 16.8%], Z = 4.02, p = 5e-5), and a 10.1% reduction in children 5-15 (95% CI: [8.5%, 11.8%], Z = 4.7, p = 2e-5). All ages residents of the MDA + IRS arm enjoyed an overall 80.1% reduction (95% CI: [80.0%, 83.0%], p = 0.0001) in odds of qPCR confirmed malaria compared with SOC residents. Secondary difference-in-difference analyses comparing surveys at different timepoints to baseline showed aOR (MDA + IRS vs IRS) of qPCR positivity between 0.28 and 0.66 (p < 0.001). Of three serious adverse events, one (nonfatal) was considered related to study medications. Limitations include the initial non-random assignment of study arms, the single large cluster per arm, and the lack of an MDA-only arm, considered to violate equipoise. CONCLUSIONS: Despite being assessed at long time points 5-7 months post-round, MDA plus IRS provided significant additional protection from malaria infection over IRS alone. Randomized trials of MDA in large areas undergoing IRS recommended as well as cohort studies of impact on incidence. TRIAL REGISTRATION: This trial was retrospectively registered 11/07/2018 with the Pan African Clinical Trials Registry (PACTR201807166695568).

COVID-19 pandemic and the global economic situation: a year on.

A year after the World Health Organization´s declaration of the novel COVID-19 as a pandemic, the global macro-economic landscape has experienced severe shocks. As a result, developed and developing countries have been saddled with intense economic uncertainties. In this study, we discuss the global macro-economic environment during the pandemic declaration period and juxtapose it with the one-year post-pandemic declaration. The evidence shows significant negative impacts on macro-economic variables in the year of the declaration. However, signs of recovery are evident a year on, albeit slowly. To sustain and accelerate the recovery gains, we suggest that strategic macro-management policies are designed and strictly implemented. Anything short of this will see especially fragile countries plunged into an "economic abyss" with severe sociopolitical implications.

Does efficiency influence firm investment size? Evidence from Ghana.

Firms commit huge funds into investment in order to maintain their operational capacity, going concern, and to maximize shareholder value. This action in many instances defines the success of the firm. The question is, How do efficiency and free cash flow associate with firm investment? This paper employs fixed and random effects to answer how technical efficiency estimates using the Data Envelopment Analysis technique and free cash flow influence firm investment. The data was sourced from published annual reports of sampled listed companies. The findings are that technically efficient firms tend to decrease firm investment. Free cash flow to the firm matters in the case of depreciation and amortization for firms. There exists a direct relationship between big size firms and firm investment. Increasing inflation and a depreciating domestic currency do not induce firm investment. The study recommends that inefficient firms must reduce their input mix to match the given level of output if they are to create value for shareholders through reduced firm investment. This article links technical efficiency to firm investment and depreciation and amortization of listed banks and manufacturing firms and is the first of its kind on Ghana.

Geostatistical modelling enables efficient safety assessment for mass drug administration with ivermectin in Loa loa endemic areas through a combined antibody and LoaScope testing strategy for elimination of onchocerciasis.

The elimination of onchocerciasis through community-based Mass Drug Administration (MDA) of ivermectin (Mectizan) is hampered by co-endemicity of Loa loa, as individuals who are highly co-infected with Loa loa parasites can suffer serious and occasionally fatal neurological reactions from the drug. The test-and-not-treat strategy of testing all individuals participating in MDA has some operational constraints including the cost and limited availability of LoaScope diagnostic tools. As a result, a Loa loa Antibody (Ab) Rapid Test was developed to offer a complementary way of determining the prevalence of loiasis. We develop a joint geostatistical modelling framework for the analysis of Ab and Loascope data to delineate whether an area is safe for MDA. Our results support the use of a two-stage strategy, in which Ab testing is used to identify areas that, with acceptably high probability, are safe or unsafe for MDA, followed by Loascope testing in areas whose safety status is uncertain. This work therefore contributes to the global effort towards the elimination of onchocerciasis as a public health problem by potentially reducing the time and cost required to establish whether an area is safe for MDA.

Model-based geostatistics enables more precise estimates of neglected tropical-disease prevalence in elimination settings: mapping trachoma prevalence in Ethiopia.

BACKGROUND: As the prevalences of neglected tropical diseases reduce to low levels in some countries, policymakers require precise disease estimates to decide whether the set public health targets have been met. At low prevalence levels, traditional statistical methods produce imprecise estimates. More modern geospatial statistical methods can deliver the required level of precision for accurate decision-making. METHODS: Using spatially referenced data from 3567 cluster locations in Ethiopia in the years 2017, 2018 and 2019, we developed a geostatistical model to estimate the prevalence of trachomatous trichiasis and to calculate the probability that the trachomatous trichiasis component of the elimination of trachoma as a public health problem has already been achieved for each of 482 evaluation units. We also compared the precision of traditional and geostatistical approaches by the ratios of the lengths of their 95% predictive intervals. RESULTS: The elimination threshold of trachomatous trichiasis (prevalence ≤ 0.2% in individuals aged ≥15 years) is met with a probability of 0.9 or more in 8 out of the 482 evaluation units assessed, and with a probability of ≤0.1 in 469 evaluation units. For the remaining five evaluation units, the probability of elimination is between 0.45 and 0.65. Prevalence estimates were, on average, 10 times more precise than estimates obtained using the traditional approach. CONCLUSIONS: By accounting for and exploiting spatial correlation in the prevalence data, we achieved remarkably improved precision of prevalence estimates compared with the traditional approach. The geostatistical approach also delivers predictions for unsampled evaluation units that are geographically close to sampled evaluation units.

MBGapp: A Shiny application for teaching model-based geostatistics to population health scientists.

User-friendly interfaces have been increasingly used to facilitate the learning of advanced statistical methodology, especially for students with only minimal statistical training. In this paper, we illustrate the use of MBGapp for teaching geostatistical analysis to population health scientists. Using a case-study on Loa loa infections, we show how MBGapp can be used to teach the different stages of a geostatistical analysis in a more interactive fashion. For wider accessibility and usability, MBGapp is available as an R package and as a Shiny web-application that can be freely accessed on any web browser. In addition to MBGapp, we also present an auxiliary Shiny app, called VariagramApp, that can be used to aid the teaching of Gaussian processes in one and two dimensions using simulations.

Identifying Plasmodium falciparum transmission patterns through parasite prevalence and entomological inoculation rate.

Background: Monitoring malaria transmission is a critical component of efforts to achieve targets for elimination and eradication. Two commonly monitored metrics of transmission intensity are parasite prevalence (PR) and the entomological inoculation rate (EIR). Comparing the spatial and temporal variations in the PR and EIR of a given geographical region and modelling the relationship between the two metrics may provide a fuller picture of the malaria epidemiology of the region to inform control activities. Methods: Using geostatistical methods, we compare the spatial and temporal patterns of Plasmodium falciparum EIR and PR using data collected over 38 months in a rural area of Malawi. We then quantify the relationship between EIR and PR by using empirical and mechanistic statistical models. Results: Hotspots identified through the EIR and PR partly overlapped during high transmission seasons but not during low transmission seasons. The estimated relationship showed a 1-month delayed effect of EIR on PR such that at lower levels of EIR, increases in EIR are associated with rapid rise in PR, whereas at higher levels of EIR, changes in EIR do not translate into notable changes in PR. Conclusions: Our study emphasises the need for integrated malaria control strategies that combine vector and human host managements monitored by both entomological and parasitaemia indices. Funding: This work was supported by Stichting Dioraphte grant number 13050800.

Model-Based Geostatistical Methods Enable Efficient Design and Analysis of Prevalence Surveys for Soil-Transmitted Helminth Infection and Other Neglected Tropical Diseases.

Maps of the geographical variation in prevalence play an important role in large-scale programs for the control of neglected tropical diseases. Precontrol mapping is needed to establish the appropriate control intervention in each area of the country in question. Mapping is also needed postintervention to measure the success of control efforts. In the absence of comprehensive disease registries, mapping efforts can be informed by 2 kinds of data: empirical estimates of local prevalence obtained by testing individuals from a sample of communities within the geographical region of interest, and digital images of environmental factors that are predictive of local prevalence. In this article, we focus on the design and analysis of impact surveys, that is, prevalence surveys that are conducted postintervention with the aim of informing decisions on what further intervention, if any, is needed to achieve elimination of the disease as a public health problem. We show that geospatial statistical methods enable prevalence surveys to be designed and analyzed as efficiently as possible so as to make best use of hard-won field data. We use 3 case studies based on data from soil-transmitted helminth impact surveys in Kenya, Sierra Leone, and Zimbabwe to compare the predictive performance of model-based geostatistics with methods described in current World Health Organization (WHO) guidelines. In all 3 cases, we find that model-based geostatistics substantially outperforms the current WHO guidelines, delivering improved precision for reduced field-sampling effort. We argue from experience that similar improvements will hold for prevalence mapping of other neglected tropical diseases.

Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm.

Current methods for the design and analysis of neglected tropical disease prevalence surveys largely rely on classical survey sampling ideas that treat prevalence data from different locations as an independent random sample from the probability distribution induced by a random sampling design. We set out an alternative, explicitly geospatial paradigm that can deliver much more precise estimates of the geospatial variation in prevalence over a country or region of interest. We describe the advantages of this approach under three headings: streamlining, whereby more precise results can be obtained with smaller sample sizes; integrating, whereby a joint analysis of data from two or more diseases can bring further gains in precision; and adapting, whereby the choice of future sampling location is informed by past data.

Implications of the COVID-19 pandemic in eliminating trachoma as a public health problem.

BACKGROUND: Progress towards elimination of trachoma as a public health problem has been substantial, but the coronavirus disease 2019 (COVID-19) pandemic has disrupted community-based control efforts. METHODS: We use a susceptible-infected model to estimate the impact of delayed distribution of azithromycin treatment on the prevalence of active trachoma. RESULTS: We identify three distinct scenarios for geographic districts depending on whether the basic reproduction number and the treatment-associated reproduction number are above or below a value of 1. We find that when the basic reproduction number is <1, no significant delays in disease control will be caused. However, when the basic reproduction number is >1, significant delays can occur. In most districts, 1 y of COVID-related delay can be mitigated by a single extra round of mass drug administration. However, supercritical districts require a new paradigm of infection control because the current strategies will not eliminate disease. CONCLUSIONS: If the pandemic can motivate judicious, community-specific implementation of control strategies, global elimination of trachoma as a public health problem could be accelerated.

Predicted Impact of COVID-19 on Neglected Tropical Disease Programs and the Opportunity for Innovation.

Due to the COVID-19 pandemic, many key neglected tropical disease (NTD) activities have been postponed. This hindrance comes at a time when the NTDs are progressing towards their ambitious goals for 2030. Mathematical modelling on several NTDs, namely gambiense sleeping sickness, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiases (STH), trachoma, and visceral leishmaniasis, shows that the impact of this disruption will vary across the diseases. Programs face a risk of resurgence, which will be fastest in high-transmission areas. Furthermore, of the mass drug administration diseases, schistosomiasis, STH, and trachoma are likely to encounter faster resurgence. The case-finding diseases (gambiense sleeping sickness and visceral leishmaniasis) are likely to have fewer cases being detected but may face an increasing underlying rate of new infections. However, once programs are able to resume, there are ways to mitigate the impact and accelerate progress towards the 2030 goals.

Implications of the COVID-19 pandemic on eliminating trachoma as a public health problem.

BACKGROUND: Progress towards elimination of trachoma as a public health problem has been substantial, but the COVID-19 pandemic has disrupted community-based control efforts. METHODS: We use a susceptible-infected model to estimate the impact of delayed distribution of azithromycin treatment on the prevalence of active trachoma. RESULTS: We identify three distinct scenarios for geographic districts depending on whether the basic reproduction number and the treatment-associated reproduction number are above or below a value of one. We find that when the basic reproduction number is below one, no significant delays in disease control will be caused. However, when the basic reproduction number is above one, significant delays can occur. In most districts a year of COVID-related delay can be mitigated by a single extra round of mass drug administration. However, supercritical districts require a new paradigm of infection control because the current strategies will not eliminate disease. CONCLUSION: If the pandemic can motivate judicious, community-specific implementation of control strategies, global elimination of trachoma as a public health problem could be accelerated.

Design and Analysis of Elimination Surveys for Neglected Tropical Diseases.

As neglected tropical diseases approach elimination status, there is a need to develop efficient sampling strategies for confirmation (or not) that elimination criteria have been met. This is an inherently difficult task because the relative precision of a prevalence estimate deteriorates as prevalence decreases, and classic survey sampling strategies based on random sampling therefore require increasingly large sample sizes. More efficient strategies for survey design and analysis can be obtained by exploiting any spatial correlation in prevalence within a model-based geostatistics framework. This framework can be used for constructing predictive probability maps that can inform in-country decision makers of the likelihood that their elimination target has been met, and where to invest in additional sampling. We evaluated our methodology using a case study of lymphatic filariasis in Ghana, demonstrating that a geostatistical approach outperforms approaches currently used to determine an evaluation unit's elimination status.

A geostatistical framework for combining spatially referenced disease prevalence data from multiple diagnostics.

Multiple diagnostic tests are often used due to limited resources or because they provide complementary information on the epidemiology of a disease under investigation. Existing statistical methods to combine prevalence data from multiple diagnostics ignore the potential overdispersion induced by the spatial correlations in the data. To address this issue, we develop a geostatistical framework that allows for joint modelling of data from multiple diagnostics by considering two main classes of inferential problems: (a) to predict prevalence for a gold-standard diagnostic using low-cost and potentially biased alternative tests; (b) to carry out joint prediction of prevalence from multiple tests. We apply the proposed framework to two case studies: mapping Loa loa prevalence in Central and West Africa, using miscroscopy, and a questionnaire-based test called RAPLOA; mapping Plasmodium falciparum malaria prevalence in the highlands of Western Kenya using polymerase chain reaction and a rapid diagnostic test. We also develop a Monte Carlo procedure based on the variogram in order to identify parsimonious geostatistical models that are compatible with the data. Our study highlights (a) the importance of accounting for diagnostic-specific residual spatial variation and (b) the benefits accrued from joint geostatistical modelling so as to deliver more reliable and precise inferences on disease prevalence.

Biting patterns of malaria vectors of the lower Shire valley, southern Malawi.

Assessing the biting behaviour of malaria vectors plays an integral role in understanding the dynamics of malaria transmission in a region. Biting times and preference for biting indoors or outdoors varies among mosquito species and across regions. These behaviours may also change over time in response to vector control measures such as long-lasting insecticidal nets (LLINs). Data on these parameters can provide the sites and times at which different interventions would be effective for vector control. This study assessed the biting patterns of malaria vectors in Chikwawa district, southern Malawi. The study was conducted during the dry and wet seasons in 2016 and 2017, respectively. In each season, mosquitoes were collected indoors and outdoors for 24 nights in six houses per night using the human landing catch. Volunteers were organized into six teams of two individuals, whereby three teams collected mosquitoes indoors and the other three collected mosquitoes outdoors each night, and the teams were rotated among twelve houses. All data were analyzed using Poisson log-linear models. The most abundant species were Anopheles gambiae sensu lato (primarily An. arabiensis) and An. funestus s.l. (exclusively An. funestus s.s.). During the dry season, the biting activity of An. gambiaes.l. was constant outdoors across the categorized hours (18:00 h to 08:45 h), but highest in the late evening hours (21:00 h to 23:45 h) during the wet season. The biting activity of An. funestus s.l. was highest in the late evening hours (21:00 h to 23:45 h) during the dry season and in the late night hours (03:00 h to 05:45 h) during the wet season. Whereas the number of An. funestuss.l. biting was constant (P = 0.662) in both seasons, that of An. gambiaes.l. was higher during the wet season than in the dry season (P = 0.001). Anopheles gambiae s.l. was more likely to bite outdoors than indoors in both seasons. During the wet season, An. funestus s.l. was more likely to bite indoors than outdoors but during the dry season, the bites were similar both indoors and outdoors. The biting activity that occurred in the early and late evening hours, both indoors and outdoors coincides with the times at which individuals may still be awake and physically active, and therefore unprotected by LLINs. Additionally, a substantial number of anopheline bites occurred outdoors. These findings imply that LLINs would only provide partial protection from malaria vectors, which would affect malaria transmission in this area. Therefore, protection against bites by malaria mosquitoes in the early and late evening hours is essential and can be achieved by designing interventions that reduce vector-host contacts during this period.

Geostatistical modelling of the association between malaria and child growth in Africa.

BACKGROUND: Undernutrition among children under 5 years of age continues to be a public health challenge in many low- and middle-income countries and can lead to growth stunting. Infectious diseases may also affect child growth, however their actual impact on the latter can be difficult to quantify. In this paper, we analyse data from 20 Demographic and Health Surveys (DHS) conducted in 13 African countries to investigate the relationship between malaria and stunting. Our objective is to make inference on the association between malaria incidence during the first year of life and height-for-age Z-scores (HAZs). METHODS: We develop a geostatistical model for HAZs as a function of both measured and unmeasured child-specific and spatial risk factors. We visualize stunting risk in each of the 20 analysed surveys by mapping the predictive probability that HAZ is below - 2. Finally, we carry out a meta-analysis by modelling the estimated effects of malaria incidence on HAZ from each DHS as a linear regression on national development indicators from the World Bank. RESULTS: A non-spatial univariate linear regression of HAZ on malaria incidence showed a negative association in 18 out of 20 surveys. However, after adjusting for spatial risk factors and controlling for confounding effects, we found a weaker association between HAZ and malaria, with a mix of positive and negative estimates, of which 3 out of 20 are significantly different from zero at the conventional 5% level. The meta-analysis showed that this variation in the estimated effect of malaria incidence on HAZ is significantly associated with the amount of arable land. CONCLUSION: Confounding effects on the association between malaria and stunting vary both by country and over time. Geostatistical analysis provides a useful framework that allows to account for unmeasured spatial confounders. Establishing whether the association between malaria and stunting is causal would require longitudinal follow-up data on individual children.

Boosting antenatal care attendance and number of hospital deliveries among pregnant women in rural communities: a community initiative in Ghana based on mobile phones applications and portable ultrasound scans.

BACKGROUND: The World Health Organization has recommended at least four antenatal care (ANC) visits and skilled attendants at birth. Most pregnant women in rural communities in low-income countries do not achieve the minimum recommended visits and deliver without skilled attendants. With the aim of increasing number of ANC visits, reducing home deliveries, and supplementing care given by ANC clinics, a proposed system based on low-cost mobile phones and portable ultrasound scan machines was piloted. METHODS: A sample of 323 pregnant women from four rural communities in the Central Region of Ghana were followed within a 11-month project. In each community, at least one health worker was trained and equipped with a mobile phone to promote ANC and hospital deliveries in her own community. If women cannot attend ANC, technicians acquired scans by using portable ultrasound machines in her community directly and sent them almost in real time to be analyzed by a gynecologist in an urban hospital. A preliminary survey to assess ANC status preceding the pilot study was conducted. During this, one hundred women who had had pregnancies within five years prior to the study were interviewed. RESULTS: The preliminary survey showed that women who attended ANC were less likely to have a miscarriage and more likely to have delivery at hospital or clinic than those who did not, and women who attained at least four ANC visits were less likely to practice self-medication. Among the women involved in the project, 40 gave birth during the period of observation. The proposed prenatal care approach showed that 62.5 % of pregnant women who gave birth during the observation period included in the project (n=40) had their labor attended in clinics or hospitals as against 37.5 % among the cases reported in the pre-survey. One case of ectopic and two cases of breech pregnancies were detected during the pilot through the proposed approach, and appropriate medical interventions were sought. CONCLUSION: Our results show that the proposed prenatal care approach can make quality ANC accessible in rural communities where pregnant women have not been able to access proper ANC.