Assessing the relationship between operationally defined zero-dose communities and access to selected primary healthcare services for children and pregnant women in emergency settings.

In this study the authors examine the relationship between "zero-dose" communities and access to healthcare services. This was done by first ensuring the first dose of the Diphtheria Tetanus and Pertussis vaccine was a better measure of zero-dose communities than the measles-containing vaccine. Once ensured, it was used to examine the association with access to primary healthcare services for children and pregnant women in the Democratic Republic of Congo, Afghanistan, and Bangladesh. These services were divided into: a) unscheduled healthcare services such as birth assistance as well as seeking care and treatment for diarrheal diseases and cough/fever episodes and b) other scheduled health services such as antenatal care visits and vitamin A supplementation. Using recent Demographic Health Survey data (2014: Democratic Republic of Congo, 2015: Afghanistan, 2018: Bangladesh), data was analyzed via Chi Squared analysis or Fischer's Exact Test. If significant, a linear regression analysis was performed to examine if the association was linear. While the linear relationship observed between children who had received the first dose of the Diphtheria Tetanus and Pertussis vaccine (the reverse to zero-dose communities) and coverage of other vaccines was expected, the results of the regression analysis depicted an unexpected split in behavior. For scheduled and birth assistance health services, a linear relationship was generally observed. For unscheduled services associated with illness treatments, this was not the case. While it does not appear that the first dose of the Diphtheria Tetanus and Pertussis vaccine can be used to predict (at least in a linear manner) access to some primary (particularly illness treatment) healthcare services in emergency/ humanitarian settings, it can serve as an indirect measure of health services not associated with the treatment of childhood infections such as antenatal care, skilled birth assistance, and to a lesser degree even vitamin A supplementation.

A mathematical model to estimate the incidence of child wasting in Yemen.

INTRODUCTION: The ongoing civil war in Yemen has severely restricted imports of food and fuel, disrupted livelihoods and displaced millions, worsening already high pre-war levels of food insecurity. Paired with frequent outbreaks of disease and a collapsed health system, this has brought rates of wasting in children under five to the country's highest recorded levels, which continue to increase as the crisis worsens and aid becomes increasingly limited. In their planning of services to treat and prevent wasting in children, humanitarian agencies rely on a standard calculation to estimate the expected number of cases for the coming year, where incidence is estimated from prevalence and the average duration of an episode of wasting. The average duration of an episode of moderate and severe wasting is currently estimated at 7.5 months-a globally-used value derived from historical cohort studies. Given that incidence varies considerably by context-where food production and availability, treatment coverage and disease rates all vary-a single estimate cannot be applied to all contexts, and especially not a highly unstable crisis setting such as Yemen. While recent studies have aimed to derive context-specific incidence estimates in several countries, little has been done to estimate the incidence of both moderate and severe wasting in Yemen. METHODS: In order to provide context-specific estimates of the average duration of an episode, and resultingly, incidence correction factors for moderate and severe wasting, we have developed a Markov model. Model inputs were estimated using a combination of treatment admission and outcome records compiled by the Yemen Nutrition Cluster, 2018 and 2019 SMART surveys, and other estimates from the literature. The model derived estimates for the governorate of Lahj, Yemen; it was initialized using August 2018 SMART survey prevalence data and run until October 2019-the date of the subsequent SMART survey. Using a process of repeated model calibration, the incidence correction factors for severe wasting and moderate wasting were found, validating the resulting prevalence against the recorded value from the 2019 SMART survey. RESULTS: The average durations of an episode of moderate and severe wasting were estimated at 4.86 months, for an incidence correction factor k of 2.59, and 3.86 months, for an incidence correction factor k of 3.11, respectively. It was found that the annual caseload of moderate wasting was 36% higher and the annual caseload of severe wasting 58% higher than the originally-assumed values, estimated with k = 1.6. CONCLUSION: The model-derived incidence rates, consistent with findings from other contexts that a global incidence correction factor cannot be sufficient, allow for improved, context-specific estimates of the burden of wasting in Yemen. In crisis settings such as Yemen where funding and resources are extremely limited, the model's outputs holistically capture the burden of wasting in a way that may guide effective decision-making and may help ensure that limited resources are allocated most effectively.

Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater.

Although wastewater and sewage systems are known to be significant reservoirs of antibiotic-resistant bacterial populations and periodic outbreaks of drug-resistant infection, there is little quantitative understanding of the drivers behind resistant population growth in these settings. In order to fill this gap in quantitative understanding of the development of antibiotic-resistant infections in wastewater, we have developed a mathematical model synthesizing many known drivers of antibiotic resistance in these settings to help predict the growth of resistant populations in different environmental scenarios. A number of these drivers of drug-resistant infection outbreak, including antibiotic residue concentration, antibiotic interaction, chromosomal mutation, and horizontal gene transfer, have not previously been integrated into a single computational model. We validated the outputs of the model with quantitative studies conducted on the eVOLVER continuous culture platform. Our integrated model shows that low levels of antibiotic residues present in wastewater can lead to increased development of resistant populations and that the dominant mechanism of resistance acquisition in these populations is horizontal gene transfer rather than acquisition of chromosomal mutations. Additionally, we found that synergistic antibiotics at low concentrations lead to increased resistant population growth. These findings, consistent with recent experimental and field studies, provide new quantitative knowledge on the evolution of antibiotic-resistant bacterial reservoirs, and the model developed herein can be adapted for use as a prediction tool in public health policy making, particularly in low-income settings where water sanitation issues remain widespread and disease outbreaks continue to undermine public health efforts. IMPORTANCE The rate at which antimicrobial resistance (AMR) has developed and spread throughout the world has increased in recent years, and according to the Review on Antimicrobial Resistance in 2014, it is suggested that the current rate will lead to AMR-related deaths of several million people by 2050 (Review on Antimicrobial Resistance, Tackling a Crisis for the Health and Wealth of Nations, 2014). One major reservoir of resistant bacterial populations that has been linked to outbreaks of drug-resistant bacterial infections but is not well understood is in wastewater settings, where antibiotic pollution is often present. Using ordinary differential equations incorporating several known drivers of resistance in wastewater, we find that interactions between antibiotic residues and horizontal gene transfer significantly affect the growth of resistant bacterial reservoirs.

Economic Evaluation of Screening Interventions for Drug-induced Liver Injury.

BACKGROUND: Human immunodeficiency virus (HIV)/AIDS and tuberculosis (TB) continue to be a significant global burden, disproportionately affecting low- and middle-income countries (LMICs). While much progress has been made in treating these epidemics, this has led to a rise in liver complications, as patients on ARTs and anti-TBs are at an increased risk of drug-induced liver injury (DILI). Therefore, patients on these medicines require consistent screening of liver function. Due to logistical barriers, gold standard DILI screening fails to be executed at the point-of-care in LMICs. For this reason, we used cost-effectiveness analysis to gauge the efficacy of a paper-test that could be implemented in these settings. METHODS: We used a Markov Model to simulate HIV and TB coinfected patient care in LMICs using both publicly available data and data from Village Health Works in Burundi. We compared the cost-effectiveness of two screening interventions for liver function monitoring: 1. paper-based point-of-care testing, and 2. gold-standard laboratory testing. These interventions were compared against baseline clinical monitoring. RESULTS: The paper test showed a 56% increase in efficacy over clinical monitoring alone. The paper-test is more cost-effective than the gold-standard method, at a ceiling cost of $1.60 per test. CONCLUSIONS: With this information, policy makers can be informed as to the large potential value of paper-based tests when gold standard monitoring is not achievable. Scientists and engineers should also keep these analyses in mind and while in development limit the cost of an ALT screening test to $1.60.

A predictive model for healthcare coverage in Yemen.

INTRODUCTION: The ongoing war in Yemen continues to pose challenges for healthcare coverage in the country especially with regards to critical gaps in information systems needed for planning and delivering health services. Restricted access to social services including safe drinking water and sanitation systems have likely led to an increase in the spread of diarrheal diseases which remains one of greatest sources of mortality in children under 5 years old. To overcome morbidity and mortality from diarrheal diseases among children in the context of severe information shortages, a predictive model is needed to determine the burden of diarrheal disease on Yemeni children and their ability to reach curative health services through an estimate of healthcare coverage. This will allow for national and local health authorities and humanitarian partners to make better informed decisions for planning and providing health care services. METHODS: A probabilistic Markov model was developed based on an analysis of Yemen's health facilities' clinical register data provided by UNICEF. The model combines this health system data with environmental and conflict-related factors such as the destruction of infrastructure (roads and health facilities) to fill in gaps in population-level data on the burden of diarrheal diseases on children under five, and the coverage rate of the under-five sick population with treatment services at primary care facilities. The model also provides estimates of the incidence rate, and treatment outcomes including treatment efficacy and mortality rate. RESULTS: By using alternatives to traditional healthcare data, the model was able to recreate the observed trends in treatment with no significant difference compared to provided validation data. Once validated, the model was used to predict the percent of sick children with diarrhea who were able to reach, and thus receive, treatment services (coverage rate) for 2019 which ranged between an average weekly minimum of 1.73% around the 28th week of the year to a weekly maximum coverage of just over 5% around the new year. These predictions can be translated into policy decisions such as when increased efforts are needed to reach children and what type of service delivery modalities may be the most effective. CONCLUSION: The model developed and presented in this manuscript shows a seasonal trend in the spread of diarrheal disease in children under five living in Yemen through a novel incorporation of weather, infrastructure and conflict parameters in the model. Our model also provides new information on the number of children seeking treatment and how this is influenced by the ongoing conflict. Despite the work of the national and local health authorities with the support of aid organizations, during the mid-year rains up to 98% of children with diarrhea are unable to receive treatment services. Thus, it is recommended that community outreach or other delivery modalities through which services are delivered in closer proximity to those in need should be scaled up prior to and during these periods. This would serve to increase number of children able to receive treatment by lessening the prohibitive travel burden, or access constraint, on families during these times.

The role of assembly parameters on polyplex poly(beta-amino ester) nanoparticle transfections.

Intracellular delivery of nucleic acids to mammalian cells using polyplex nanoparticles (NPs) remains a challenge both in vitro and in vivo, with transfections often suffering from variable efficacy. To improve reproducibility and efficacy of transfections in vitro using a next-generation polyplex transfection material poly(beta-amino ester)s (PBAEs), the influence of multiple variables in the preparation of these NPs on their transfection efficacy was explored. The results indicate that even though PBAE/pDNA polyplex NPs are formed by the self-assembly of polyelectrolytes, their transfection is not affected by the manner in which the components are mixed, facilitating self-assembly in a single step, but timing for self-assembly of 5-20 min is optimal. In addition, even though the biomaterials are biodegradable in water, their efficacy is not affected by up to eight freeze-thaw cycles of the polymer. It was found that there is a greater stability of nucleic acid-complexed polymer as a polyplex nanoparticle compared with free polymer. Finally, by exploring multiple buffer systems, it was identified that utilization of divalent cation magnesium or calcium acetate buffers at pH 5.0 is optimal for transfection using these polymeric materials, boosting transfection several folds compared with monovalent cations. Together, these results can improve the reproducibility and efficacy of PBAE and similar polyplex nanoparticle transfections and improve the robustness of using these biomaterials for bioengineering and biotechnology applications.

A combinatorial library of biodegradable polyesters enables non-viral gene delivery to post-mitotic human stem cell-derived polarized RPE monolayers.

Safe and effective delivery of DNA to post-mitotic cells, especially highly differentiated cells, remains a challenge despite significant progress in the development of gene delivery tools. Biodegradable polymeric nanoparticles (NPs) offer an array of advantages for gene delivery over viral vectors due to improved safety, carrying capacity, ease of manufacture, and cell-type specificity. Here we demonstrate the use of a high-throughput screening (HTS) platform to synthesize and screen a library of 148 biodegradable polymeric nanoparticles, successfully identifying structures that enable efficient transfection of human pluripotent stem cell differentiated human retinal pigment epithelial (RPE) cells with minimal toxicity. These NPs can deliver plasmid DNA (pDNA) to RPE monolayers more efficiently than leading commercially available transfection reagents. Novel synthetic polymers are described that enable high efficacy non-viral gene delivery to hard-to-transfect polarized human RPE monolayers, enabling gene loss- and gain-of-function studies of cell signaling, developmental, and disease-related pathways. One new synthetic polymer in particular, 3,3'-iminobis(N,N-dimethylpropylamine)-end terminated poly(1,5-pentanediol diacrylate-co-3 amino-1-propanol) (5-3-J12), was found to form self-assembled nanoparticles when mixed with plasmid DNA that transfect a majority of these human post-mitotic cells with minimal cytotoxicity. The platform described here can be utilized as an enabling technology for gene transfer to human primary and stem cell-derived cells, which are often fragile and resistant to conventional gene transfer approaches.

Continuous microfluidic assembly of biodegradable poly(beta-amino ester)/DNA nanoparticles for enhanced gene delivery.

Translation of biomaterial-based nanoparticle formulations to the clinic faces significant challenges including efficacy, safety, consistency and scale-up of manufacturing, and stability during long-term storage. Continuous microfluidic fabrication of polymeric nanoparticles has the potential to alleviate the challenges associated with manufacture, while offering a scalable solution for clinical level production. Poly(beta-amino esters) (PBAE)s are a class of biodegradable cationic polymers that self-assemble with anionic plasmid DNA to form polyplex nanoparticles that have been shown to be effective for transfecting cancer cells specifically in vitro and in vivo. Here, we demonstrate the use of a microfluidic device for the continuous and scalable production of PBAE/DNA nanoparticles followed by lyophilization and long term storage that results in improved in vitro efficacy in multiple cancer cell lines compared to nanoparticles produced by bulk mixing as well as in comparison to widely used commercially available transfection reagents polyethylenimine and Lipofectamine® 2000. We further characterized the nanoparticles using nanoparticle tracking analysis (NTA) to show that microfluidic mixing resulted in fewer DNA-free polymeric nanoparticles compared to those produced by bulk mixing. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 1813-1825, 2017.