Role of seasonal importation and genetic drift on selection for drug-resistant genotypes of Plasmodium falciparum in high-transmission settings.

Historically Plasmodium falciparum has followed a pattern of drug resistance first appearing in low-transmission settings before spreading to high-transmission settings. Several features of low-transmission regions are hypothesized as explanations: higher chance of symptoms and treatment seeking, better treatment access, less within-host competition among clones and lower rates of recombination. Here, we test whether importation of drug-resistant parasites is more likely to lead to successful emergence and establishment in low-transmission or high-transmission periods of the same epidemiological setting, using a spatial, individual-based stochastic model of malaria and drug-resistance evolution calibrated for Burkina Faso. Upon controlling for the timing of importation of drug-resistant genotypes and examination of key model variables, we found that drug-resistant genotypes imported during the low-transmission season were (i) more susceptible to stochastic extinction due to the action of genetic drift, and (ii) more likely to lead to establishment of drug resistance when parasites are able to survive early stochastic loss due to drift. This implies that rare importation events are more likely to lead to establishment if they occur during a high-transmission season, but that constant importation (e.g. neighbouring countries with high levels of resistance) may produce a greater risk during low-transmission periods.

Influenza vaccination allocation in tropical settings under constrained resources.

Influenza virus seasonality, synchronicity, and vaccine supply differ substantially between temperate and tropical settings, and optimal vaccination strategy may differ on this basis. Most national vaccine recommendations focus on high-risk groups, elderly populations, and healthcare workers despite previous analyses demonstrating broad benefits to vaccinating younger high-contact age groups. Here, we parameterized an age-structured non-seasonal asynchronous epidemiological model of influenza virus transmission for a tropical low-income setting. We evaluated timing and age allocation of vaccines across vaccine supplies ranging from 10% to 90% using decade-based age groups. Year-round vaccination was beneficial when comparing to vaccination strategies focused on a particular time of year. When targeting a single age-group for vaccine prioritization, maximum vaccine allocation to the 10-19 high-contact age group minimized annual influenza mortality for all but one vaccine supply. When evaluating across all possible age allocations, optimal strategies always allocated a plurality of vaccines to school-age children (10-19). The converse however was not true as not all strategies allocating a plurality to children aged 10-19 minimized mortality. Allocating a high proportion of vaccine supply to the 10-19 age group is necessary but not sufficient to minimize annual mortality as distribution of remaining vaccine doses to other age groups also needs to be optimized. Strategies focusing on indirect benefits (vaccinating children) showed higher variance in mortality outcomes than strategies focusing on direct benefits (vaccinating the elderly). However, the indirect benefit approaches showed lower mean mortality and lower minimum mortality than vaccination focused on the elderly.

Role of Seasonal Importation and Random Genetic Drift on Selection for Drug-Resistant Genotypes of Plasmodium falciparum in High Transmission Settings.

Historically Plasmodium falciparum has followed a pattern of drug resistance first appearing in low transmission settings before spreading to high transmission settings. Several features of low-transmission regions are hypothesized as explanations: higher chance of symptoms and treatment seeking, better treatment access, less within-host competition among clones, and lower rates of recombination. Here, we test whether importation of drug-resistant parasites is more likely to lead to successful emergence and establishment in low-transmission or high-transmission periods of the same epidemiological setting, using a spatial, individual-based stochastic model of malaria and drug-resistance evolution calibrated for Burkina Faso. Upon controlling for the timing of importation of drug-resistant genotypes and examination of key model variables, we found that drug-resistant genotypes imported during the low transmission season were, (1) more susceptible to stochastic extinction due to the action of random genetic drift, and (2) more likely to lead to establishment of drug resistance when parasites are able to survive early stochastic loss due to drift. This implies that rare importation events are more likely to lead to establishment if they occur during a high-transmission season, but that constant importation (e.g., neighboring countries with high levels of resistance) may produce a greater risk during low-transmission periods.

A combination of annual and nonannual forces drive respiratory disease in the tropics.

INTRODUCTION: It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics is less well understood. In this study, we aimed to characterise the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City, Vietnam. METHODS: We monitored the daily number of ILI patients in 89 outpatient clinics from January 2010 to December 2019. We collected nasal swabs and tested for influenza from a subset of clinics from May 2012 to December 2019. We used spectral analysis to describe the periodic signals in the system. We evaluated the contribution of these periodic signals to predicting ILI and influenza patterns through lognormal and gamma hurdle models. RESULTS: During 10 years of community surveillance, 66 799 ILI reports were collected covering 2.9 million patient visits; 2604 nasal swabs were collected, 559 of which were PCR-positive for influenza virus. Both annual and nonannual cycles were detected in the ILI time series, with the annual cycle showing 8.9% lower ILI activity (95% CI 8.8% to 9.0%) from February 24 to May 15. Nonannual cycles had substantial explanatory power for ILI trends (ΔAIC=183) compared with all annual covariates (ΔAIC=263) in lognormal regression. Near-annual signals were observed for PCR-confirmed influenza but were not consistent over time or across influenza (sub)types. The explanatory power of climate factors for ILI and influenza virus trends was weak. CONCLUSION: Our study reveals a unique pattern of respiratory disease dynamics in a tropical setting influenced by both annual and nonannual drivers, with influenza dynamics showing near-annual periodicities. Timing of vaccination campaigns and hospital capacity planning may require a complex forecasting approach.

Weekly Forecasting of Yellow Fever Occurrence and Incidence via Eco-Meteorological Dynamics.

Yellow Fever (YF), a mosquito-borne disease, requires ongoing surveillance and prevention due to its persistence and ability to cause major epidemics, including one that began in Brazil in 2016. Forecasting based on factors influencing YF risk can improve efficiency in prevention. This study aimed to produce weekly forecasts of YF occurrence and incidence in Brazil using weekly meteorological and ecohydrological conditions. Occurrence was forecast as the probability of observing any cases, and incidence was forecast to represent morbidity if YF occurs. We fit gamma hurdle models, selecting predictors from several meteorological and ecohydrological factors, based on forecast accuracy defined by receiver operator characteristic curves and mean absolute error. We fit separate models for data before and after the start of the 2016 outbreak, forecasting occurrence and incidence for all municipalities of Brazil weekly. Different predictor sets were found to produce most accurate forecasts in each time period, and forecast accuracy was high for both time periods. Temperature, precipitation, and previous YF burden were most influential predictors among models. Minimum, maximum, mean, and range of weekly temperature, precipitation, and humidity contributed to forecasts, with optimal lag times of 2, 6, and 7 weeks depending on time period. Results from this study show the use of environmental predictors in providing regular forecasts of YF burden and producing nationwide forecasts. Weekly forecasts, which can be produced using the forecast model developed in this study, are beneficial for informing immediate preparedness measures.

Benefits of near-universal vaccination and treatment access to manage COVID-19 burden in the United States.

BACKGROUND: As we continue the fourth year of the COVID-19 epidemic, SARS-CoV-2 infections still cause high morbidity and mortality in the United States. During 2020-2022, COVID-19 was one of the leading causes of death in the United States and by far the leading cause among infectious diseases. Vaccination uptake remains low despite this being an effective burden reducing intervention. The development of COVID-19 therapeutics provides hope for mitigating severe clinical outcomes. This modeling study examines combined strategies of vaccination and treatment to reduce the burden of COVID-19 epidemics over the next decade. METHODS: We use a validated mathematical model to evaluate the reduction of incident cases, hospitalized cases, and deaths in the United States through 2033 under various levels of vaccination and treatment coverage. We assume that future seasonal transmission patterns for COVID-19 will be similar to those of influenza virus and account for the waning of infection-induced immunity and vaccine-induced immunity in a future with stable COVID-19 dynamics. Due to uncertainty in the duration of immunity following vaccination or infection, we consider three exponentially distributed waning rates, with means of 365 days (1 year), 548 days (1.5 years), and 730 days (2 years). We also consider treatment failure, including rebound frequency, as a possible treatment outcome. RESULTS: As expected, universal vaccination is projected to eliminate transmission and mortality. Under current treatment coverage (13.7%) and vaccination coverage (49%), averages of 81,000-164,600 annual reported deaths, depending on duration of immunity, are expected by the end of this decade. Annual mortality in the United States can be reduced below 50,000 per year with 52-80% annual vaccination coverage and below 10,000 annual deaths with 59-83% annual vaccination coverage, depending on duration of immunity. Universal treatment reduces hospitalizations by 88.6% and deaths by 93.1% under current vaccination coverage. A reduction in vaccination coverage requires a comparatively larger increase in treatment coverage in order for hospitalization and mortality levels to remain unchanged. CONCLUSIONS: Adopting universal vaccination and universal treatment goals in the United States will likely lead to a COVID-19 mortality burden below 50,000 deaths per year, a burden comparable to that of influenza virus.

Repeatability and timing of tropical influenza epidemics.

Much of the world experiences influenza in yearly recurring seasons, particularly in temperate areas. These patterns can be considered repeatable if they occur predictably and consistently at the same time of year. In tropical areas, including southeast Asia, timing of influenza epidemics is less consistent, leading to a lack of consensus regarding whether influenza is repeatable. This study aimed to assess repeatability of influenza in Vietnam, with repeatability defined as seasonality that occurs at a consistent time of year with low variation. We developed a mathematical model incorporating parameters to represent periods of increased transmission and then fitted the model to data collected from sentinel hospitals throughout Vietnam as well as four temperate locations. We fitted the model for individual (sub)types of influenza as well as all combined influenza throughout northern, central, and southern Vietnam. Repeatability was evaluated through the variance of the timings of peak transmission. Model fits from Vietnam show high variance (sd = 64-179 days) in peak transmission timing, with peaks occurring at irregular intervals and throughout different times of year. Fits from temperate locations showed regular, annual epidemics in winter months, with low variance in peak timings (sd = 32-57 days). This suggests that influenza patterns are not repeatable or seasonal in Vietnam. Influenza prevention in Vietnam therefore cannot rely on anticipation of regularly occurring outbreaks.

A combination of annual and nonannual forces drive respiratory disease in the tropics.

Background: It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics remains elusive. In this study, we aimed to characterize the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City (HCMC), Vietnam. Methods: We monitored the daily number of ILI patients in 89 outpatient clinics from January 2010 to December 2019. We collected nasal swabs and tested for influenza from a subset of clinics from May 2012 to December 2019. We used spectral analysis to describe the periodicities in the system. We evaluated the contribution of these periodicities to predicting ILI and influenza patterns through lognormal and gamma hurdle models. Findings: During ten years of community surveillance, 66,799 ILI reports were collected covering 2.9 million patient visits; 2604 nasal swabs were collected 559 of which were PCR-positive for influenza virus. Both annual and nonannual cycles were detected in the ILI time series, with the annual cycle showing 8.9% lower ILI activity (95% CI: 8.8%-9.0%) from February 24 to May 15. Nonannual cycles had substantial explanatory power for ILI trends (ΔAIC = 183) compared to all annual covariates (ΔAIC = 263). Near-annual signals were observed for PCR-confirmed influenza but were not consistent along in time or across influenza (sub)types. Interpretation: Our study reveals a unique pattern of respiratory disease dynamics in a tropical setting influenced by both annual and nonannual drivers. Timing of vaccination campaigns and hospital capacity planning may require a complex forecasting approach.

Benefits of near-universal vaccination and treatment access to manage COVID-19 burden in the United States.

Background: As we enter the fourth year of the COVID-19 pandemic, SARS-CoV-2 infections still cause high morbidity and mortality in the United States. During 2020-2022, COVID-19 was one of the leading causes of death in the United States and by far the leading cause among infectious diseases. Vaccination uptake remains low despite this being an effective burden reducing intervention. The development of COVID-19 therapeutics provides hope for mitigating severe clinical outcomes. This modeling study examines combined strategies of vaccination and treatment to reduce the burden of COVID-19 epidemics over the next decade. Methods: We use a validated mathematical model to evaluate the reduction of incident cases, hospitalized cases, and deaths in the United States through 2033 under various levels of vaccination and treatment coverage. We assume that future seasonal transmission patterns for COVID-19 will be similar to those of influenza virus. We account for the waning of infection-induced immunity and vaccine-induced immunity in a future with stable COVID-19 dynamics. Due to uncertainty in the duration of immunity following vaccination or infection, we consider two exponentially-distributed waning rates, with means of 365 days (one year) and 548 days (1.5 years). We also consider treatment failure, including rebound frequency, as a possible treatment outcome. Results: As expected, universal vaccination is projected to eliminate transmission and mortality. Under current treatment coverage (13.7%) and vaccination coverage (49%), averages of 89,000 annual deaths (548-day waning) and 120,000 annual deaths (365-day waning) are expected by the end of this decade. Annual mortality in the United States can be reduced below 50,000 per year with >81% annual vaccination coverage, and below 10,000 annual deaths with >84% annual vaccination coverage. Universal treatment reduces hospitalizations by 88% and deaths by 93% under current vaccination coverage. A reduction in vaccination coverage requires a comparatively larger increase in treatment coverage in order for hospitalization and mortality levels to remain unchanged. Conclusions: Adopting universal vaccination and universal treatment goals in the United States will likely lead to a COVID-19 mortality burden below 50,000 deaths per year, a burden comparable to that of influenza virus.

Quantitative risk assessment model to investigate the public health impact of varying Listeria monocytogenes allowable levels in different food commodities: A retrospective analysis.

Invasive listeriosis is a potentially fatal foodborne disease that according to this study may affect up to 32.9 % of the US population considered as increased risk and including people with underlying conditions and co-morbidities. Listeria monocytogenes has been scrutinized in research and surveillance programs worldwide in Ready-to-Eat (RTE) food commodities (RTE salads, deli meats, soft/semi-soft cheese, seafood) and frozen vegetables in the last 30 years with an estimated overall prevalence of 1.4-9.9 % worldwide (WD) and 0.5-3.8 % in the United States (US). Current L. monocytogenes control efforts have led to a prevalence reduction in the last 5 years of 4.9-62.9 % (WD) and 12.4-92.7 % (US). A quantitative risk assessment model was developed, estimating the probability of infection in the US susceptible population to be 10-10,000× higher than general population and the total number of estimated cases in the US was 1044 and 2089 cases by using the FAO/WHO and Pouillot dose-response models. Most cases were attributed to deli meats (>90 % of cases) followed by RTE salads (3.9-4.5 %), soft and semi-soft cheese and RTE seafood (0.5-1.0 %) and frozen vegetables (0.2-0.3 %). Cases attributed to the increased risk population corresponded to 96.6-98.0 % of the total cases with the highly susceptible population responsible for 46.9-80.1 % of the cases. Removing product lots with a concentration higher than 1 CFU/g reduced the prevalence of contamination by 15.7-88.3 % and number of cases by 55.9-100 %. Introducing lot-by-lot testing and defining allowable quantitative regulatory limits for low-risk RTE commodities may reduce the public health impact of L. monocytogenes and improve the availability of enumeration data.

Environmental determinants predicting population vulnerability to high yellow fever incidence.

Yellow fever (YF) is an endemic mosquito-borne disease in Brazil, though many locations have not observed cases in recent decades. Some locations with low disease burden may resemble locations with higher disease burden through environmental and ecohydrological characteristics, which are known to impact YF burden, motivating increased or continued prevention measures such as vaccination, mosquito control or surveillance. This study aimed to use environmental characteristics to estimate vulnerability to observing high YF burden among all Brazilian municipalities. Vulnerability was defined in three categories based on yearly incidence between 2000 and 2017: minimal, low and high vulnerability. A cumulative logit model was fit to these categories using environmental and ecohydrological predictors, selecting those that provided the most accurate model fit. Per cent of days with precipitation, mean temperature, biome, population density, elevation, vegetation and nearby disease occurrence were included in best-fitting models. Model results were applied to estimate vulnerability nationwide. Municipalities with highest probability of observing high vulnerability was found in the North and Central-West (2000-2016) as well as the Southeast (2017) regions. Results of this study serve to identify specific locations to prioritize new or ongoing surveillance and prevention of YF based on underlying ecohydrological conditions.

Estimating case fatality risk of severe Yellow Fever cases: systematic literature review and meta-analysis.

BACKGROUND: Case fatality risk (CFR), commonly referred to as a case fatality ratio or rate, represents the probability of a disease case being fatal. It is often estimated for various diseases through analysis of surveillance data, case reports, or record examinations. Reported CFR values for Yellow Fever vary, offering wide ranges. Estimates have not been found through systematic literature review, which has been used to estimate CFR of other diseases. This study aims to estimate the case fatality risk of severe Yellow Fever cases through a systematic literature review and meta-analysis. METHODS: A search strategy was implemented in PubMed and Ovid Medline in June 2019 and updated in March 2021, seeking reported severe case counts, defined by fever and either jaundice or hemorrhaging, and the number of those that were fatal. The searches yielded 1,133 studies, and title/abstract review followed by full text review produced 14 articles reporting 32 proportions of fatal cases, 26 of which were suitable for meta-analysis. Four studies with one proportion each were added to include clinical case data from the recent outbreak in Brazil. Data were analyzed through an intercept-only logistic meta-regression with random effects for study. Values of the I2 statistic measured heterogeneity across studies. RESULTS: The estimated CFR was 39 % (95 % CI: 31 %, 47 %). Stratifying by continent showed that South America observed a higher CFR than Africa, though fewer studies reported estimates for South America. No difference was seen between studies reporting surveillance data and studies investigating outbreaks, and no difference was seen among different symptom definitions. High heterogeneity was observed across studies. CONCLUSIONS: Approximately 39 % of severe Yellow Fever cases are estimated to be fatal. This study provides the first systematic literature review to estimate the CFR of Yellow Fever, which can provide insight into outbreak preparedness and estimating underreporting.

A chemical prioritization process: Applications to contaminants of emerging concern in freshwater ecosystems (Phase I).

Contaminants of emerging concern (CECs), such as pharmaceuticals, personal care products, and hormones, are frequently found in aquatic ecosystems around the world. Information on sublethal effects from exposure to commonly detected concentrations of CECs is lacking and the limited availability of toxicity data makes it difficult to interpret the biological significance of occurrence data. However, the ability to evaluate the effects of CECs on aquatic ecosystems is growing in importance, as detection frequency increases. The goal of this study was to prioritize the chemical hazards of 117 CECs detected in subsistence species and freshwater ecosystems on the Grand Portage Indian Reservation and adjacent 1854 Ceded Territory in Minnesota, USA. To prioritize CECs for management actions, we adapted Minnesota Pollution Control Agency's Aquatic Toxicity Profiles framework, a tool for the rapid assessment of contaminants to cause adverse effects on aquatic life by incorporating chemical-specific information. This study aimed to 1) perform a rapid-screening assessment and prioritization of detected CECs based on their potential environmental hazard; 2) identify waterbodies in the study region that contain high priority CECs; and 3) inform future monitoring, assessment, and potential remediation in the study region. In water samples alone, 50 CECs were deemed high priority. Twenty-one CECs were high priority among sediment samples and seven CECs were high priority in fish samples. Azithromycin, DEET, diphenhydramine, fluoxetine, miconazole, and verapamil were high priority in all three media. Due to the presence of high priority CECs throughout the study region, we recommend future monitoring of particular CECs based on the prioritization method used here. We present an application of a chemical hazard prioritization process and identify areas where the framework may be adapted to meet the objectives of other management-related assessments.

Anthropogenic factors associated with contaminants of emerging concern detected in inland Minnesota lakes (Phase II).

Contaminants of emerging concern (CECs) include a variety of pharmaceuticals, personal care products, and hormones commonly detected in surface waters. Human activities, such as wastewater treatment and discharge, contribute to the distribution of CECs in water, but other sources and pathways are less frequently examined. This study aimed to identify anthropogenic activities and environmental characteristics associated with the presence of CECs, previously determined to be of high priority for further research and mitigation, in rural inland lakes in northeastern Minnesota, United States. The setting for this study consisted of 21 lakes located within both the Grand Portage Indian Reservation and the 1854 Ceded Territory, where subsistence hunting and fishing are important to the cultural heritage of the indigenous community. We used data pertaining to numbers of buildings, healthcare facilities, wastewater treatment plants, impervious surfaces, and wetlands within defined areas surrounding the lakes as potential predictors of the detection of high priority CECs in water, sediment, and fish. Separate models were run for each contaminant detected in each sample media. We used least absolute shrinkage and selection operator (LASSO) models to account for both predictor selection and parameter estimation for CEC detection. Across contaminants and sample media, the percentage of impervious surface was consistently positively associated with CEC detection. Number of buildings in the surrounding area was often negatively associated with CEC detection, though nonsignificant. Surrounding population, presence of wastewater treatment facilities, and percentage of wetlands in surrounding areas were positively, but inconsistently, associated with CECs, while catchment area and healthcare centers were generally not associated. The results of this study highlight human activities and environmental characteristics associated with CEC presence in a rural area, informing future work regarding specific sources and transport pathways. We also demonstrate the utility of LASSO modeling in the identification of these important relationships.

Information differences across spatial resolutions and scales for disease surveillance and analysis: The case of Visceral Leishmaniasis in Brazil.

Nationwide disease surveillance at a high spatial resolution is desired for many infectious diseases, including Visceral Leishmaniasis. Statistical and mathematical models using data collected from surveillance activities often use a spatial resolution and scale either constrained by data availability or chosen arbitrarily. Sensitivity of model results to the choice of spatial resolution and scale is not, however, frequently evaluated. This study aims to determine if the choice of spatial resolution and scale are likely to impact statistical and mathematical analyses. Visceral Leishmaniasis in Brazil is used as a case study. Probabilistic characteristics of disease incidence, representing a likely outcome in a model, are compared across spatial resolutions and scales. Best fitting distributions were fit to annual incidence from 2004 to 2014 by municipality and by state. Best fits were defined as the distribution family and parameterization minimizing the sum of absolute error, evaluated through a simulated annealing algorithm. Gamma and Poisson distributions provided best fits for incidence, both among individual states and nationwide. Comparisons of distributions using Kullback-Leibler divergence shows that incidence by state and by municipality do not follow distributions that provide equivalent information. Few states with Gamma distributed incidence follow a distribution closely resembling that for national incidence. These results demonstrate empirically how choice of spatial resolution and scale can impact mathematical and statistical models.

Occurrence of contaminants of emerging concern in aquatic ecosystems utilized by Minnesota tribal communities.

Pharmaceuticals, personal care products, hormones, and other chemicals lacking water quality standards are frequently found in surface water. While evidence is growing that these contaminants of emerging concern (CECs) - those previously unknown, unrecognized, or unregulated - can affect the behavior and reproduction of fish and wildlife, little is known about the distribution of these chemicals in rural, tribal areas. Therefore, we surveyed the presence of CECs in water, sediment, and subsistence fish species across various waterbodies, categorized as undeveloped (i.e., no human development along shorelines), developed (i.e., human development along shorelines), and wastewater effluent-impacted (i.e., contain effluence from wastewater treatment plants), within the Grand Portage Indian Reservation and 1854 Ceded Territory in northeastern Minnesota, U.S.A. Overall, in 28 sites across three years (2016-2018), 117 of the 158 compounds tested were detected in at least one form of medium (i.e., water, sediment, or fish). CECs were detected most frequently at wastewater effluent-impacted sites, with up to 83 chemicals detected in one such lake, while as many as 17 were detected in an undeveloped lake. Although there was no statistically significant difference between the number of CECs present in developed versus undeveloped lakes, a range of 3-17 CECs were detected across these locations. Twenty-two CECs were detected in developed and undeveloped sites that were not detected in wastewater effluent-impacted sites. The detection of CECs in remote, undeveloped locations, where subsistence fish are harvested, raises scientific questions about the safety and security of subsistence foods for indigenous communities. Further investigation is warranted so that science-based solutions to reduce chemical risks to aquatic life and people can be developed locally and be informative for indigenous communities elsewhere.

Demographic Inequities in Health Outcomes and Air Pollution Exposure in the Atlanta Area and its Relationship to Urban Infrastructure.

Environmental burdens such as air pollution are inequitably distributed with groups of lower socioeconomic statuses, which tend to comprise of large proportions of racial minorities, typically bearing greater exposure. Such groups have also been shown to present more severe health outcomes which can be related to adverse pollution exposure. Air pollution exposure, especially in urban areas, is usually impacted by the built environment, such as major roadways, which can be a significant source of air pollution. This study aims to examine inequities in prevalence of cardiovascular and respiratory diseases in the Atlanta metropolitan region as they relate to exposure to air pollution and characteristics of the built environment. Census tract level data were obtained from multiple sources to model health outcomes (asthma, chronic obstructive pulmonary disease, coronary heart disease, and stroke), pollution exposure (particulate matter and nitrogen oxides), demographics (ethnicity and proportion of elderly residents), and infrastructure characteristics (tree canopy cover, access to green space, and road intersection density). Conditional autoregressive models were fit to the data to account for spatial autocorrelation among census tracts. The statistical model showed areas with majority African-American populations had significantly higher exposure to both air pollutants and higher prevalence of each disease. When considering univariate associations between pollution and health outcomes, the only significant association existed between nitrogen oxides and COPD being negatively correlated. Greater percent tree canopy cover and green space access were associated with higher prevalence of COPD, CHD, and stroke. Overall, in considering health outcomes in connection with pollution exposure infrastructure and ethnic demographics, demographics remained the most significant explanatory variable.

Effect of Mobile Device-Supported Single-Patient Multi-crossover Trials on Treatment of Chronic Musculoskeletal Pain: A Randomized Clinical Trial.

Importance: Individually designed single-patient multi-crossover (n-of-1) trials can facilitate tailoring of treatments directed at various conditions, including chronic musculoskeletal pain (CMSP) but are potentially burdensome, which may limit uptake in research and practice. Objectives: To determine whether patients randomized to participate in an n-of-1 trial supported by a mobile health (mHealth) app would experience less pain and improved global health, adherence, satisfaction, and shared decision making compared with patients assigned to usual care. Design, Setting, and Participants: This randomized clinical trial compared participation in an individualized, mHealth-supported n-of-1 trial vs usual care. The participating 215 patients had CMSP for at least 6 weeks, had a smartphone or tablet with a data plan, were enrolled in northern California from July 2014 through July 2016, and were followed for up to 1 year by 48 clinicians in academic, community, Veterans Affairs, and military settings. Interventions: Intervention patients met with their clinicians and used a desktop interface to select treatments and trial parameters for an n-of-1 trial comparing 2 pain-management regimens. The mHealth app provided reminders to take designated treatments on assigned days and to upload responses to daily questions on pain and treatment-associated adverse effects. Control patients received care as usual. Main Outcomes and Measures: The primary outcome was change in the PROMIS (Patient-Reported Outcomes Measurement Information System) pain-related interference 8-item short-form scale (full scale range, 41-78) from baseline to 6 months. Secondary outcomes included patient-reported pain intensity, overall health, analgesic adherence, trust in clinician, satisfaction with care, medication-related shared decision making, and, for the n-of-1 group only, participant engagement and experience. Results: Among 215 patients (108 randomized to the n-of-1 intervention and 107 to control), 102 (47%) were women, and the mean (SD) age was 55.5 (11.1) years. At the 6-month follow-up, pain interference was reduced in both groups, though there was no difference between the intervention and control groups (-1.36 points; 95% CI, -2.91 to 0.19 points; P = .09). There were no advantages in secondary outcomes for intervention patients vs control patients except for higher medication-related shared decision making at 6 months (between-group difference, 11.9 points; 95% CI, 2.6-21.2 points; P = .01). Among patients assigned to the n-of-1 group, 88% (n = 86) affirmed that the mHealth app could help people like them manage their pain. Conclusions and Relevance: In this population of patients with CMSP, mHealth-supported n-of-1 trials were feasible and associated with a satisfactory user experience, but n-of-1 trial participation did not significantly improve pain interference at 6 months vs usual care. Trial Registration: ClinicalTrials.gov identifier: NCT02116621.

Optimal information networks: Application for data-driven integrated health in populations.

Development of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and redundancy among variables, instead considering only individual correlations. In addition, a unified method for assessing integrated health statuses of populations is lacking, making systematic comparison among populations impossible. We propose the use of maximum entropy networks (MENets) that use transfer entropy to assess interrelatedness among selected variables considered for inclusion in a composite indicator. We also define optimal information networks (OINs) that are scale-invariant MENets, which use the information in constructed networks for optimal decision-making. Health outcome data from multiple cities in the United States are applied to this method to create a systemic health indicator, representing integrated health in a city.

Climate patterns and mosquito-borne disease outbreaks in South and Southeast Asia.

BACKGROUND: Vector-borne infectious diseases, particularly mosquito-borne, pose a substantial threat to populations throughout South and Southeast Asia. Outbreaks have affected this region several times during the early years of the 21st century, notably through outbreaks of Chikungunya and Dengue. These diseases are believed to be highly prevalent at endemic levels in the region as well. With a changing global climate, the impacts of changes in ambient temperatures and precipitation levels on mosquito populations are important for understanding the effects on risk of mosquito-borne disease outbreaks. This study aims to make use of a large data set to determine how risk of mosquito-borne infectious disease outbreaks relates to the highest monthly average temperature and precipitation for each year in South and Southeast Asia. METHODS: Generalized additive models were used in a marked point process to fit nonlinear trends relating temperature and precipitation to outbreak risk, fitting splines for temperature and precipitation. Confounding factors for nation affluence, climate type, and ability to report outbreaks were also included. RESULTS: Parabolic trends for both temperature and precipitation were observed relating to outbreak risk. The trend for temperature, which was significant, showed that outbreak risk peaks near 33.5°C as the highest monthly average temperature. Though not significant, a trend for precipitation was observed showing risk peaking when the highest monthly average precipitation is 650mm. CONCLUSIONS: Peak levels of temperature and precipitation were identified for outbreak risk. These findings support the notion of a poleward shift in the distribution of mosquitoes within this region rather than a poleward expansion in geographic range.