Distance sampling for epidemiology: an interactive tool for estimating under-reporting of cases from clinic data

Background: Distance sampling methods are widely used in ecology to estimate and map the abundance of animal and plant populations from spatial survey data. The key underlying concept in distance sampling is the detection function, the probability of detecting the occurrence of an event as a function of its distance from the observer, as well as other covariates that may influence detection. In epidemiology, the burden and distribution of infectious disease is often inferred from cases that are reported at clinics and hospitals. In areas with few public health facilities and low accessibility, the probability of detecting a case is also a function of the distance between an infected person and the “observer” (e.g. a health centre). While the problem of distance-related under-reporting is acknowledged in public health; there are few quantitative methods for assessing and correcting for this bias when mapping disease incidence. Here, we develop a modified version of distance sampling for prediction of infectious disease incidence by relaxing some of the framework’s fundamental assumptions. We illustrate the utility of this approach using as our example malaria distribution in rural Burkina Faso, where there is a large population at risk but relatively low accessibility of health facilities. Results: The modified distance-sampling framework was used to predict the probability of reporting malaria infection at 8 rural clinics, based on road-travel distances from villages. The rate at which reporting probability dropped with distance varied between clinics, depending on road and clinic positions. The probability of case detection was estimated as 0.3–1 in the immediate vicinity of the clinic, dropping to 0.1–0.6 at a travel distance of 10 km, and effectively zero at distances > 30–40 km. Conclusions: To enhance the method’s strategic impact, we provide an interactive mapping tool (as a self-contained R Shiny app) that can be used by non-specialists to interrogate model outputs and visualize how the overall probability of under-reporting and the catchment area of each clinic is influenced by changing the number and spatial allocation of health centres

Solutions to Facility Location–Network Design Problems

This doctoral thesis presents new solution strategies for facility location–network design (FLND) problems. FLND is a combination of facility location and network design: the overall goal is to improve clients’ access to facilities and the means of reaching this goal include both building facilities (as in facility location) and building travelable links (as in network design). We measure clients’ access to facilities by the sum of the travel costs, and our objective is to minimize this sum. FLND problems have facility location problems and network design problems, both of which are NP-hard, as subproblems and are therefore themselves theoretically difficult problems. We approach the search for optimal solutions from both above and below, contributing techniques for finding good upper bounds as well as good lower bounds on an optimal solution. On the upper bound side, we present the first heuristics in the literature for this problem. We have developed a variety of heuristics: simple greedy heuristics, a local search heuristic, metaheuristics including simulated annealing and variable neighborhood search, as well as a custom heuristic based on the problem-specific structure of FLND. Our computational results compare the performance of these heuristics and show that the basic variable neighborhood search performs the best, achieving a solution within 0.6% of optimality on average for our test cases. On the lower bound side, we work with an existing IP formulation whose LP relaxation provides good lower bounds. We present a separation routine for a new class of inequalities that further improve the lower bound, in some cases even obtaining the optimal solution. Putting all this together, we develop a branch-and-cut approach that uses heuristic solutions as upper bounds, and cutting planes for increasing the lower bound at each node of the problem tree, thus reducing the number of nodes needed to solve to optimality. We also present an alternate IP formulation that uses fewer variables than the one accepted in the literature. This formulation allows some problems to be solved more quickly, although its LP relaxation is not as tight. To aid in the visualization of FLND problem instances and their solutions, we have developed a piece of software, FLND Visualizer. Using this application one can create and modify problem instances, solve using a variety of heuristic methods, and view the solutions. Finally, we consider a case study: improving access to health facilities in the Nouna health district of Burkina Faso. We demonstrate the solution techniques developed here on this real-world problem and show the remarkable improvements in accessibility that are possible

A heuristic approach to solve the preventive health care problem with budget and congestion constraints

This document is the Accepted Manuscript version of the following article: Soheil Davari, Kemal Kilic, and Siamak Naderi, ‘A heuristic approach to solve the preventive health care problem with budget and congestion constraints’, Applied Mathematics and Computation, Vol. 276, pp. 442-453, March 2016, doi: https://doi.org/10.1016/j.amc.2015.11.073. This manuscript version is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License CC BY NC-ND 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.Preventive health care is of utmost importance to governments since they can make massive savings on health care expenditure and promote the well-being of the society. Preventive care includes many services such as cancer screenings, vaccinations, hepatitis screenings, and smoking cessation programs. Despite the benefits of these services, their uptake is not satisfactory in many countries in the world. This can be attributed to financial barriers, social issues., and other factors. One of the most important barriers for preventive care is accessibility to proper services, which is a function of various qualitative and quantitative factors such as the distance to travel, waiting time, vicinity of facilities to other attractive facilities (such as shopping malls), and even the cleanliness of the facilities. Statistics show that even a small improvement in people’s participation can save massive amounts of money for any government and improve the well-being of the people in a society. This paper addresses the problem of designing a preventive health care network considering impatient clients, and budget constraints. The objective is to maximize the accessibility of services to people. We model the problem as a mixed-integer programming problem with budget constraints, and congestion considerations. An efficient variable neighborhood search procedure is proposed and computational experiments are performed on a large set of instances.Peer reviewedFinal Accepted Versio

Clustering of mortality among children under five years due to malaria at the Ifakara demographic surveillance site in Tanzania

ABSTRACT Introduction Under-five mortality is still a major cause of concern in Sub Saharan Africa and among the highest in the world. This is also exacerbated by the high prevalence and episodes of malaria in this age group, which accounts for 90% of all under-five deaths estimated in the region annually. The effect of detecting clustering of all cause and cause specific mortality and underlying factors is crucial for timely public health interventions. This is especially important for health authorities in Tanzania where under-five malaria attributable deaths accounts for 45% of the annual estimated mortality of 100, 000. Study objectives To estimate under-five mortality and analyze clustering of all cause and malaria specific mortality among under five children in Ifakara Demographic Surveillance System from 2002-2005. Methods Data from the Ifakara Health Research and Development Centre (IHRDC) were obtained for all under-five children who lived in 25 villages in the DSS from 2002 – 2005. Analyses for all cause and malaria cause specific under-five mortality were done using data collected from the DSS and verbal autopsy systems. Annual all cause and malaria specific mortality rates were calculated by dividing number of deaths and person years observed. Clustering of deaths for all cause and cause specific (malaria) in the 25 villages were analyzed using SaTScanTM version 7.0 software. A Poisson model was used to detect clusters with high rates in space and in space-time. Household assets and characteristics were used to construct a wealth index using Principal component analysis (PCA) in StataTM version9. The index was used to group households into five equal groups from poorest to least poor. Results Overall infants’ mortality was sixty-three times higher (326 per 1,000 person years) compared to children (5.1 per 1,000 person years) and with mortality rates between girls and boys were very similar, (15.8 and 14.8 per 1,000 person years). Year of death and place of death (village) were found to be significantly associated with malaria deaths. However, socio-economic status of parents in households where deaths occurred was not associated to malaria deaths in the DSS. A number of statistically significant clusters of all cause and cause specific malaria deaths were identified in several locations in the DSS. The located clusters imply that villages within the clusters have an elevated risk of under-five deaths. A space-time cluster of four villages with radius of 15.91 km was discovered with the highest risk (RR 2.71; P-value 0.020) of malaria deaths in 2004. Conclusion These findings demonstrate that there is non-random clustering of both all cause and malaria cause specific mortality in the study area. The high infant mortality results also suggest a careful examination of the data collection procedures in the DSS and require further studies to understand this pattern of mortality among the under-five population. Appropriate health interventions aimed at reducing burden of malaria should be strengthened in this part of rural Tanzania. There is need to replicate this study to other areas in the country

Comparative Study of GIS and Conventional Household Survey Sampling Methods: Feasibility, Cost and Family Planning Coverage Estimates

Background Household surveys serve as the main source of data on reproductive, maternal, and child health in low and middle-income countries (LMICs). Considering their significant role, ensuring production of high-quality data is imperative. However, the high costs associated with conducting large-scale surveys in LMICs has led to a search for alternative survey sampling methods. This study compared two probability sampling methods: geographic information system (GIS) and conventional sampling. It assessed feasibility of GIS sampling, evaluated equivalence of sampling methods for selected family planning (FP) coverage indicators, and compared implementation costs. Methods Concurrent cross-sectional surveys using the two sampling methods were implemented in the same 150 clusters in Burkina Faso. For GIS method, free satellite images were used to digitize cluster boundaries and potentially residential structures. Feasibility was assessed using embedded mixed methods. Equivalence threshold (+/- 5 percentage points) to compare FP indicators was defined using confidence interval (CI) approach. Costs were estimated using micro-costing from international donor’s perspective. Average and incremental costs-per-cluster and costs-per-completed-interview were calculated. Results In conventional method, 14,610 households were enumerated; 3,021 households sampled. In GIS method, 58,120 structures were digitized; 3,371 households sampled. There was no statistically significant difference in the survey response rates for occupied dwellings among the two sampling methods (p=0.089). Qualitative results documented the advantages and challenges experienced during implementation of GIS method. Of the 9,907 eligible women selected, 4,370 were in conventional method, 3,913 in GIS and 1,624 in both methods. The CIs of sociodemographic variables and FP indicators overlapped across both methods. Sampling methods yielded equivalent estimates of modern contraceptive prevalence and unmet need for FP. Cost difference between the methods was $43,529. Relative to conventional method, GIS method was 15$266 and $314 less per cluster, and$13 and $4 less per completed interview, in the urban and rural areas, respectively. Conclusion Using GIS for large-scale, probability-based household surveys is feasible in both urban and rural settings, if recent, high-resolution satellite images are available. It should be considered a valid alternative for deriving unbiased population coverage estimates in resource-constrained settings

Improving quality, timeliness and efficacy of data collection and management in population-based surveillance of vital events

Electronic data collection (EDC), has become familiar in recent years, and has been quickly adopted in many research fields. It has become commonplace to assume that systems that entail entering data in mobile devices, connected through secure networks to central servers are of higher standard than old paper based data collection systems (PDC). Although the notion that EDC performs better than PDC seems reasonable and is widely accepted, few studies have tried to formally evaluate whether it can improve data quality, and none of these to our knowledge, are in the context of population-based longitudinal surveillance. This thesis project aims to assess the strength of OpenHDS, a system based on EDC, used in the population-based surveillance of vital events via Health and Demographic surveillance systems (HDSS). HDSS are both sources of vital event data and have the potential to support health intervention studies in the areas where they operate. Setting up and running an HDSS is operationally challenging, and a reliable and efficient platform for data collection and management is a basic part of it. There are often major shortcomings in the data collection and management processes in running HDSS, though these have not been extensively documented. Recent technological advances, specifically the use of mobile devices for data collection, and the adoption of OpenHDS software for data management, which makes use of best practices for data management, appear to have the potential to resolve many of these issues. The INDEPTH Network and others have invested substantial resources in the roll-out and support of OpenHDS, and there is anecdotal evidence that this has resulted in improvements, but there is considerable demand for compelling evidence. The Swiss Tropical and Public Health Institute (Swiss TPH) has supported some INDEPTH sites to fully migrate to OpenHDS (Ifakara and Rufiji in Tanzania, Nanoro in Burkina Faso, Manhiça in Mozambique and Cross river in Nigeria) and some are in the migration process (7 sites in Ethiopia: Arba Minch, Butajira, Dabat, Gilgel Gibe, Kersa and Kilite Awlaelo). Some other sites are at different stages of evaluating the possibility of adopting OpenHDS (Navrongo in Ghana, Niakhar in Senegal, Iganga/Mayuge in Uganda, Nouna in Burkina Faso, Birbhum in India etc.) and there is a demand from all of them for evidence of the benefits of adopting this system. Demonstration of the appropriate functioning of the OpenHDS is also highly relevant in the light of recently proposed approaches for comprehensive health and epidemiological surveillance systems. Such systems will need to satisfy requirements in terms of data availability and integration which are considerable higher than in a classical HDSS. This project assesses the benefits of OpenHDS in terms of and how the advances in data collection and management translate into improved data quality and timeliness. It asks whether the system architecture of the novel data management system can be further exploited to enable data integration approaches for near time quality control and near time response triggers. It also considers what are the main challenges in implementing such technologies in a new or an existing HDSS. This entails: • A description of the new system and of a set of conjectured data management best practices. For each of these best practices there is a literature review to assess if there is evidence to support it and if OpenHDS follow these practices, giving evidence of how this can be feasible and implemented in the field in two different real-life scenarios: the setting up of a new HDSS (Rusinga Island, Western Kenya and Majete Malaria Project, southern Malawi); and the migration of existing HDSSs (Ifakara, Tanzania and Nanoro, Burkina Faso) to OpenHDS. (Chapter 1) • Describing a novel approach for data collection and management in health and demographic surveillance designed to address the shortcomings of the traditional approach (OpenHDS) and documenting the usage of this system the establishment of a new HDSS (Rusinga) in Chapter 2 and 3. • Evaluating innovative approaches for quality control measures that are made possible by the novel data system architecture (in particular, use of satellite imagery to assess completeness of populations, using Majete HDSS as an example) in Chapter 4. • Studying the potential benefits of electronic data collection (compared with paper) in terms of quality, timeliness, and costs by comparing both in a contemporaneous comparison of different systems in 8 villages in Nanoro, Burkina Faso and using historical comparisons of data quality (as assessed by iSHARE2) before and after migration to OpenHDS for a range of INDEPTH sites in Chapter 5. A series of analyses were carried out to demonstrate that the OpenHDS data system for HDSSs can be implemented in both existing or newly established sites in low- and middle-income countries, and to test the hypothesis that the system is superior to previous approaches with regard of quality and timeliness of data and running costs of the system. This involved describing the novel approach to data collection and management enabled by OpenHDS, evaluating benefits in terms of quality and timeliness of the data using the OpenHDS mobile electronic data system, and the cost of electronic data collection (OpenHDS) vs. paper. It also involved evaluating the impact on the quality of the data of near-time availability and the potential of the OpenHDS system architecture for data integration for next-generation quality control and surveillance-response applications. This work demonstrates that OpenHDS is a system that manages data in a standard reference format, using rigorous checks on demographic events, adding the flexibility to introduce entire questionnaires, variables that a longitudinal study could require, and that OpenHDS can take over old demographic surveillance systems with this new real-time low-cost paperless technology opportunity to abandon old fashion research systems, that remain in use in developing countries.

Assessing performance in health care: A mathematical programming approach for the re-design of primary health care networks

Mathematical models allow studying complex systems. In particular, optimal facility location models provide a sound framework to assess the performance of first-level of health care networks. In this work, a methodology founded on need/offer/demand quantification through a facility location-based mathematical model is proposed to assess the performance of existing networks of Primary Health Care Centers (PHCC) and assist in its re-design. The proposed re-design problem investigates the re-allocation of existing resources within the given infrastructure (existing PHCCs) to better satisfy the estimated health needs of the target population. This problem has not been widely addressed in the open literature despite its paramount importance in modern societies with fast demographic dynamics and constrained investment capacities. The model seeks to optimally assign the required type of service and the corresponding capacity to each PHCC (offer). The objective function to be maximized is the number of (needed) patients’ visits effectively covered by the network (demand). The following constraints are explicitly considered: i) geographic accessibility from need centers to PHCCs, ii) maximum delivery capacity of each service in each PHCC, and iii) total budget regarding fixed, variable, and relocation costs. The proposed methodology was applied to a medium-size city. Results show that the non-attended necessity can be reduced by introducing capacity modifications in the existing network. Moreover, different solutions are obtained if budgetary restrictions or minimum attention volume constraints are included. This reveals how model-based decision support tools can help health decision-makers assessing primary health care network performance.Fil: Elorza, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Moscoso, Nebel Silvana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentin

The role of the National Health insurance scheme in shaping equity of access to healthcare in Ghana

In light of recent emphasis on achieving Universal Health Coverage through social health insurance in low income countries, this thesis examined how the National Health Insurance Scheme in Ghana impacts on equity of access to healthcare in Tamale District of northern Ghana. Using mainly a qualitative approach, the thesis specifically examined whether the NHIS promotes equity in health insurance coverage and whether insured members are able to access healthcare equitably. Against this background, four broad findings were identified. Firstly, even though the NHIS improved insurance coverage in the Tamale District, enrolment was largely inequitable because most socially disadvantaged groups/individuals were less able to insure. This was mainly because such groups were predisposed to developing low willingness and low ability to enrol in the NHIS as a result of their individual and community characteristics as well as NHIS and healthcare system factors. Secondly, the NHIS improved the affordability of healthcare services and reduced the risk of catastrophic healthcare expenditure among insured members, particularly insured low income households. Thirdly, while the NHIS improved the financial resources of healthcare providers and the availability of medicines and medical supplies, it adversely impacted on the general quality of healthcare services mainly because the supply of healthcare resources failed to keep up with a high demand for healthcare services by insured members. Fourthly, the NHIS also improved the use of formal care, particularly among insured low income households due to their greater healthcare needs and previous inability to afford the cost of healthcare services. However, due to long waiting times associated with accessing NHIS healthcare, the improvement in financial access to healthcare by the NHIS failed to eradicate the use of ‘informal’ forms of care (e.g. drugstore, herbal/traditional medicine) among insured members. Based on these findings, this thesis concludes that the NHIS could enhance equity in access to care if there are opportunities created to enable socially disadvantaged groups to enrol in the scheme as well as improve the availability and quality of healthcare services for insured members

Space time geography of Malaria and the environmental risks to households, Lagos State, Nigeria

Phd ThesisThe research employs the theoretical lens of human ecology of disease to examine the ecology of malaria in Lagos state, Nigeria. As a first step I examine the spatial and temporal trends in clinical malaria infection using a density-based algorithm to identify two locations (Ikeja and Kosofe LGAs) with one of the highest malaria infection rates and ecologically diverse terrain. They form the focus of this research. I gather data and derive measures on 26 theoretically relevant environment and socio-cultural risk variables in a cross-section of 208 households using mixed methods that comprise semi-structured interviews, a questionnaire, environmental observations, GIS and remote sensing data and GPS mapping. Through these efforts, I build a household spatial database. I assess the contributory influences of the risk variables through the development and assessment of ten ecologically relevant candidate models of urban malaria using statistical and GIS analysis. I also engage with the everyday lives of the households and qualify the quantitative relationships. Findings reveal that the most parsimonious candidate model is grounded on the human ecology of disease principle. While many of the variables are not statistically significant, some, such as travel history, animal presence and household size, are of public health importance. One important finding emerges. The risk variable “working at night without mosquito protection”, though it does not appear in this model, seems to be important across other models. I examine it further and note that its risk within households is higher than those associated with residential locations. In fact, households inhabit low-risk locations and have low vulnerability risk rates. This suggests that in urban areas, infection likely occurs outside homes and mostly from places of work or social gathering, and coincides with older household members rather than vulnerable children. This research suggests further insights for urban-like occupations and behaviours.Dorothy Hodgkin Postgraduate Award (NERC & Shell BP), Newcastle University and the Nigerian Petroleum Technology Development Fund