Effects Of Alternative Food Voucher Delivery Strategies On Nutrition Among Low-Income Adults

Nutrition assistance programs are the subject of ongoing policy debates. Two proposals remain uninformed by existing evidence: whether restricting benefits to allow only fruit and vegetable purchases improves overall dietary intake, and whether more frequent distribution of benefits (weekly versus monthly) induces more fruit and vegetable consumption and less purchasing of calorie-dense foods. In a community-based trial, we randomly assigned participants to receive food vouchers that differed in what foods could be purchased (fruit and vegetables only or any foods) and in distribution schedule (in weekly or monthly installments, holding total monthly value constant). The use of vouchers for fruit and vegetables only did not yield significantly greater improvements than the unrestricted voucher did in terms of fruit and vegetable consumption or Healthy Eating Index (HEI) score. Weekly vouchers also failed to yield significantly greater improvements than monthly vouchers did. Proposed policies to make assistance more restricted or more frequent, while holding benefit value constant, might not improve nutrition among low-income Americans.

Frequency of routine testing for COVID-19 in high-risk healthcare environments to reduce outbreaks

Routine asymptomatic testing strategies for COVID-19 have been proposed to prevent outbreaks in high-risk healthcare environments. We used simulation modeling to evaluate the optimal frequency of viral testing. We found that routine testing substantially reduces risk of outbreaks, but may need to be as frequent as twice weekly.

Heterogeneity in the Effects of Food Vouchers on Nutrition Among Low-Income Adults: A Quantile Regression Analysis

PURPOSE

To determine whether baseline fruit and vegetable (FV) intake or other predictors are associated with response to food vouchers (change in FV intake) among low-income adults.

DESIGN

Secondary analysis of a randomized, 2 x 2-factorial, community-based trial.

SETTING

San Francisco, California.

SUBJECTS

359 low-income adults aged ≥21 years old.

INTERVENTION

Participants were mailed $20 of food vouchers monthly for 6 months, and randomized to 1 of 4 arms according to: eligible foods (FV only or any foods) and redemption schedule (weekly or monthly).

MEASURES

Change in FV intake measured in cup equivalents between baseline and month 6 of the trial, based on 24-hour dietary recalls.

ANALYSIS

Quantile multivariate regressions were employed to measure associations between key predictors and change in FV intake across study arms.

RESULTS

FV-only weekly vouchers were associated with increased FV intake at the 25th percentile (0.24 cups/day, p = 0.048) and 50th percentile (0.37 cups/day, p = 0.02) of the distribution, but not at lower and higher quantiles. Response to the vouchers diminished 0.10 cups/day for each additional household member (p = 0.02).

CONCLUSION

Response to food vouchers varied along the FV intake distribution, pointing to some more responsive groups and others potentially needing additional support to increase FV intake. Larger households likely need vouchers of higher dollar value to result in similar changes in dietary intake as that observed in smaller households.

Mapping geographical inequalities in access to drinking water and sanitation facilities in low-income and middle-income countries, 2000-17

BACKGROUND

Universal access to safe drinking water and sanitation facilities is an essential human right, recognised in the Sustainable Development Goals as crucial for preventing disease and improving human wellbeing. Comprehensive, high-resolution estimates are important to inform progress towards achieving this goal. We aimed to produce high-resolution geospatial estimates of access to drinking water and sanitation facilities.

METHODS

We used a Bayesian geostatistical model and data from 600 sources across more than 88 low-income and middle-income countries (LMICs) to estimate access to drinking water and sanitation facilities on continuous continent-wide surfaces from 2000 to 2017, and aggregated results to policy-relevant administrative units. We estimated mutually exclusive and collectively exhaustive subcategories of facilities for drinking water (piped water on or off premises, other improved facilities, unimproved, and surface water) and sanitation facilities (septic or sewer sanitation, other improved, unimproved, and open defecation) with use of ordinal regression. We also estimated the number of diarrhoeal deaths in children younger than 5 years attributed to unsafe facilities and estimated deaths that were averted by increased access to safe facilities in 2017, and analysed geographical inequality in access within LMICs.

FINDINGS

Across LMICs, access to both piped water and improved water overall increased between 2000 and 2017, with progress varying spatially. For piped water, the safest water facility type, access increased from 40·0% (95% uncertainty interval [UI] 39·4-40·7) to 50·3% (50·0-50·5), but was lowest in sub-Saharan Africa, where access to piped water was mostly concentrated in urban centres. Access to both sewer or septic sanitation and improved sanitation overall also increased across all LMICs during the study period. For sewer or septic sanitation, access was 46·3% (95% UI 46·1-46·5) in 2017, compared with 28·7% (28·5-29·0) in 2000. Although some units improved access to the safest drinking water or sanitation facilities since 2000, a large absolute number of people continued to not have access in several units with high access to such facilities (>80%) in 2017. More than 253 000 people did not have access to sewer or septic sanitation facilities in the city of Harare, Zimbabwe, despite 88·6% (95% UI 87·2-89·7) access overall. Many units were able to transition from the least safe facilities in 2000 to safe facilities by 2017; for units in which populations primarily practised open defecation in 2000, 686 (95% UI 664-711) of the 1830 (1797-1863) units transitioned to the use of improved sanitation. Geographical disparities in access to improved water across units decreased in 76·1% (95% UI 71·6-80·7) of countries from 2000 to 2017, and in 53·9% (50·6-59·6) of countries for access to improved sanitation, but remained evident subnationally in most countries in 2017.

INTERPRETATION

Our estimates, combined with geospatial trends in diarrhoeal burden, identify where efforts to increase access to safe drinking water and sanitation facilities are most needed. By highlighting areas with successful approaches or in need of targeted interventions, our estimates can enable precision public health to effectively progress towards universal access to safe water and sanitation.

FUNDING

Bill & Melinda Gates Foundation.

Understanding the Mechanisms Underlying Host Restriction of Insect-Specific Viruses

Arthropod-borne viruses contribute significantly to global mortality and morbidity in humans and animals. These viruses are mainly transmitted between susceptible vertebrate hosts by hematophagous arthropod vectors, especially mosquitoes. Recently, there has been substantial attention for a novel group of viruses, referred to as insect-specific viruses (ISVs) which are exclusively maintained in mosquito populations. Recent discoveries of novel insect-specific viruses over the past years generated a great interest not only in their potential use as vaccine and diagnostic platforms but also as novel biological control agents due to their ability to modulate arbovirus transmission. While arboviruses infect both vertebrate and invertebrate hosts, the replication of insect-specific viruses is restricted in vertebrates at multiple stages of virus replication. The vertebrate restriction factors include the genetic elements of ISVs (structural and non-structural genes and the untranslated terminal regions), vertebrate host factors (agonists and antagonists), and the temperature-dependent microenvironment. A better understanding of these bottlenecks is thus warranted. In this review, we explore these factors and the complex interplay between ISVs and their hosts contributing to this host restriction phenomenon.

Design of a cluster-randomized trial of the effectiveness and cost-effectiveness of metformin on prevention of type 2 diabetes among prediabetic Mexican adults (the PRuDENTE initiative of Mexico City)

INTRODUCTION

Type 2 diabetes (T2D) is a global epidemic, and nations are struggling to implement effective healthcare strategies to reduce the burden. While efficacy studies demonstrate that metformin can reduce incident T2D by half among younger, obese adults with prediabetes, its real-world effectiveness are understudied, and its use for T2D prevention in primary care is low. We describe the design of a pragmatic trial to evaluate the incremental effectiveness of metformin, as an adjunct to a simple lifestyle counseling.

METHODS

The "Prevención de la Diabetes con Ejercicio, Nutrición y Tratamiento" [Diabetes Prevention with Exercise, Nutrition and Treatment; PRuDENTE, (Spanish acronym)] is a cluster-randomized trial in Mexico City's public primary healthcare system. The study randomly assigns 51 clinics to deliver one of two interventions for 36 months: 1) lifestyle only; 2) lifestyle plus metformin, to 3060 patients ages 30-65 with impaired fasting glucose and obesity. The primary endpoint is incident T2D (fasting glucose ≥126 mg/dL, or HbA1c ≥6.5%). We will also measure a range of implementation-related process outcomes at the clinic-, clinician- and patient-levels to inform interpretations of effectiveness and enable efforts to refine, adapt, adopt and disseminate the model. We will also estimate the cost-effectiveness of metformin as an adjunct to lifestyle counseling in Mexico.

DISCUSSION

Findings from this pragmatic trial will generate new translational knowledge in Mexico and beyond, both with respect to metformin's real-world effectiveness among an 'at-risk' population, and uncovering facilitators and barriers to the reach, adoption and implementation of metformin preventive therapy in public primary care settings.

TRIAL REGISTRATION

This trial is registered at Clinicaltrials.gov (NCT03194009).

Evolution of structural and magnetic properties of FePtCu alloy films on annealing of FePt/Cu multilayers

Thin films of ternary (FePt)100-xCux alloys were obtained by annealing of FePt (100 Å)/Cu (d Å) multilayers with d = 50 and 100 Å deposited by sputtering at room temperature on Si substrates. The evolution of structural and magnetic properties of these multilayers induced by isochronal and isothermal annealing in a vacuum has been studied using depth dependent characterization techniques. Isochronal annealing for 0.5 h at different temperatures (300 to 600 °C) showed very low interdiffusion at the interfaces with no signature of alloy phase formation. However, isothermal annealing of multilayers at 600 °C for longer times (1.5-6.5 h) showed significantly large interdiffusion accompanied by the formation of polycrystalline ternary alloy and iron silicide phases. The iron silicide formed at the substrate-film interface assists the growth of the L10 ordered ternary alloy phase, which showed different stoichiometry for different multilayers. The L10 phase formed with higher Cu content showed drastically different magnetic properties with a reduction in saturation magnetization and an increase in coercivity (∼6 kOe) at room temperature. The iron silicide formed on high temperature annealing showed ferromagnetic nature with a magnetization of ∼140 emu cm-3 at room temperature.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study

BACKGROUND

School closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.

METHODS

We provide national- and county-level simulations of school closures and unmet child care needs across the USA. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors.

RESULTS

At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.4 to 8.7%, and the effectiveness of school closures as a 7.6% and 8.4% reduction in fewer hospital and intensive care unit (ICU) beds, respectively, at peak demand when varying across initial reproduction number estimates by state. At the county level, we find substantial variations of projected unmet child care needs and school closure effects, 9.5% (interquartile range (IQR) 8.2-10.9%) of healthcare worker households and 5.2% (IQR 4.1-6.5%) and 6.8% (IQR 4.8-8.8%) reduction in fewer hospital and ICU beds, respectively, at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p<0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 76.3 to 96.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures.

CONCLUSIONS

School closures are projected to reduce peak ICU and hospital demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible trade-off between school closures and healthcare worker absenteeism.

Probing the Effects of Strong Electromagnetic Fields with Charge-Dependent Directed Flow in Pb-Pb Collisions at the LHC

The first measurement at the LHC of charge-dependent directed flow (v_{1}) relative to the spectator plane is presented for Pb-Pb collisions at sqrt[s_{NN}]=5.02  TeV. Results are reported for charged hadrons and D^{0} mesons for the transverse momentum intervals p_{T}>0.2  GeV/c and 3<p_{T}<6  GeV/c in the 5%-40% and 10%-40% centrality classes, respectively. The difference between the positively and negatively charged hadron v_{1} has a positive slope as a function of pseudorapidity η, dΔv_{1}/dη=[1.68±0.49(stat)±0.41(syst)]×10^{-4}. The same measurement for D^{0} and D[over ¯]^{0} mesons yields a positive value dΔv_{1}/dη=[4.9±1.7(stat)±0.6(syst)]×10^{-1}, which is about 3 orders of magnitude larger than the one of the charged hadrons. These measurements can provide new insights into the effects of the strong electromagnetic field and the initial tilt of matter created in noncentral heavy ion collisions on the dynamics of light (u, d, and s) and heavy (c) quarks. The large difference between the observed Δv_{1} of charged hadrons and D^{0} mesons may reflect different sensitivity of the charm and light quarks to the early time dynamics of a heavy ion collision. These observations challenge some recent theoretical calculations, which predicted a negative and an order of magnitude smaller value of dΔv_{1}/dη for both light flavor and charmed hadrons.

Evidence of Spin-Orbital Angular Momentum Interactions in Relativistic Heavy-Ion Collisions

The first evidence of spin alignment of vector mesons (K^{*0} and ϕ) in heavy-ion collisions at the Large Hadron Collider (LHC) is reported. The spin density matrix element ρ_{00} is measured at midrapidity (|y|<0.5) in Pb-Pb collisions at a center-of-mass energy (sqrt[s_{NN}]) of 2.76 TeV with the ALICE detector. ρ_{00} values are found to be less than 1/3 (1/3 implies no spin alignment) at low transverse momentum (p_{T}<2  GeV/c) for K^{*0} and ϕ at a level of 3σ and 2σ, respectively. No significant spin alignment is observed for the K_{S}^{0} meson (spin=0) in Pb-Pb collisions and for the vector mesons in pp collisions. The measured spin alignment is unexpectedly large but qualitatively consistent with the expectation from models which attribute it to a polarization of quarks in the presence of angular momentum in heavy-ion collisions and a subsequent hadronization by the process of recombination.

How close is the axillary nerve to the inferior glenoid? A magnetic resonance study of normal and arthritic shoulders

INTRODUCTION

Axillary nerve injury is a major complication of shoulder surgery during glenoid exposure. The aim of this study was to measure the mean distance between the inferior glenoid and the axillary nerve in healthy shoulders and then to compare this distance between osteoarthritic and rotator cuff deficient glenohumeral joints.

METHODS

The magnetic resonance images of 50 patients with normal glenohumeral joints were reviewed. The infra-glenoid tubercle was determined as a fixed point and the distance to the axillary nerve was measured. Two separate assessors measured on the same sagittal sections. With a study power of 80%, the sample needed in each comparison group was 28 patients. Measurements were then performed on scans in patients with osteoarthritis and cuff tear arthropathy. The mean distance was compared between groups.

RESULTS

The mean distance between the infra-glenoid tubercle and axillary nerve was 12mm (standard deviation, SD, 5.6mm) in normal shoulders, 10.6mm (SD 5.4mm) in shoulders with osteoarthritis and 9.7mm (SD 3.7mm) in those with cuff tear arthropathy. For this sample size of 50 patients with a confidence interval of 95%, the mean range is 12mm (95% CI 10.4-13.6). A comparison between normal shoulder and osteoarthritis showed a p-value of 0.3, and between normal and cuff tear arthropathy a p-value of 0.06. This was not statistically significant.

CONCLUSIONS

The axillary nerve lies on average 12mm from the infra-glenoid tubercle. The presence of inferior osteophytes in glenohumeral osteoarthritis and the proximal migration of humeral head in cuff tear arthropathy does not seem to alter the course of the nerve significantly in relation to the inferior glenoid tubercle.

Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen

OBJECTIVES

Armed conflict has contributed to an unprecedented number of internally displaced persons (IDPs), individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when IDPs will migrate to an area remains a major challenge for aid delivery organizations. We sought to develop an IDP migration forecasting framework that could empower humanitarian aid groups to more effectively allocate resources during conflicts.

METHODS

We modeled monthly IDP migration between provinces within Syria and within Yemen using data on food prices, fuel prices, wages, location, time, and conflict reports. We compared machine learning methods with baseline persistence methods of forecasting.

RESULTS

We found a machine learning approach that more accurately forecast migration trends than baseline persistence methods. A random forest model outperformed the best persistence model in terms of root mean square error of log migration by 26% and 17% for the Syria and Yemen datasets, respectively.

CONCLUSIONS

Integrating diverse data sources into a machine learning model appears to improve IDP migration prediction. Further work should examine whether implementation of such models can enable proactive aid allocation for IDPs in anticipation of forecast arrivals.

Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE

Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted.

OBJECTIVE

To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019.

EXPOSURES

Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n = 162), sertraline hydrochloride (n = 176), or extended-release venlafaxine hydrochloride (n = 180).

MAIN OUTCOMES AND MEASURES

The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index.

RESULTS

The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (C index increase, 0.012 [95% CI, 0.001-0.020]), energy loss (C index increase, 0.035 [95% CI, 0.011-0.059]), appetite changes (C index increase, 0.017 [95% CI, 0.003-0.030]), and psychomotor retardation (C index increase, 0.020 [95% CI, 0.008-0.032]).

CONCLUSIONS AND RELEVANCE

This study suggests that machine learning may be used to identify independent associations of symptoms and EEG features to predict antidepressant-associated improvements in specific symptoms of depression. The approach should next be prospectively validated in clinical trials and settings.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00693849.

Seasonal drivers of tuberculosis: evidence from over 100 years of notifications in Cape Town

BACKGROUND: Tuberculosis incidence varies seasonally in many settings. However, the role of seasonal variation in reactivation vs. transmission is unclear.METHODS: We reviewed data on TB notifications in Cape Town, South Africa, from 1903 to 2017 (exclusive of 1995-2002, which were unavailable). Data from 2003 onward were stratified by HIV status, age and notification status (new vs. retreatment). We performed seasonal decomposition and time-dependent spectral analysis using wavelets to assess periodicity over time. We estimated monthly peak-to-peak seasonal amplitude of notifications as a percentage of the annual notification rate.RESULTS: A seasonal trend was intermittently detected between 1904 and 1994, particularly during periods of high notification rates, but was consistently and strongly evident between 2003 and 2017, with peaks in September through November, following winter. Among young children, a second, higher seasonal peak was observed in March. Seasonal variation was greater in children (<5 years, 54%, 95% CI 47-61; 5-14 years, 63%, 95% CI 58-69) than in adults (36%, 95% CI 33-39).CONCLUSIONS: Stronger seasonal effects were seen in children, in whom progression following recent infection is known to be the predominant driver of disease. These findings may support increased transmission in the winter as an important driver of TB in Cape Town.

Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments

BACKGROUND

Risk adjustment models are employed to prevent adverse selection, anticipate budgetary reserve needs, and offer care management services to high-risk individuals. We aimed to address two unknowns about risk adjustment: whether machine learning (ML) and inclusion of social determinants of health (SDH) indicators improve prospective risk adjustment for health plan payments.

METHODS

We employed a 2-by-2 factorial design comparing: (i) linear regression versus ML (gradient boosting) and (ii) demographics and diagnostic codes alone, versus additional ZIP code-level SDH indicators. Healthcare claims from privately-insured US adults (2016-2017), and Census data were used for analysis. Data from 1.02 million adults were used for derivation, and data from 0.26 million to assess performance. Model performance was measured using coefficient of determination (R²), discrimination (C-statistic), and mean absolute error (MAE) for the overall population, and predictive ratio and net compensation for vulnerable subgroups. We provide 95% confidence intervals (CI) around each performance measure.

RESULTS

Linear regression without SDH indicators achieved moderate determination (R² 0.327, 95% CI: 0.300, 0.353), error ($6992; 95% CI: $6889, $7094), and discrimination (C-statistic 0.703; 95% CI: 0.701, 0.705). ML without SDH indicators improved all metrics (R² 0.388; 95% CI: 0.357, 0.420; error $6637; 95% CI: $6539, $6735; C-statistic 0.717; 95% CI: 0.715, 0.718), reducing misestimation of cost by $3.5 M per 10,000 members. Among people living in areas with high poverty, high wealth inequality, or high prevalence of uninsured, SDH indicators reduced underestimation of cost, improving the predictive ratio by 3% (~$200/person/year).

CONCLUSIONS

ML improved risk adjustment models and the incorporation of SDH indicators reduced underpayment in several vulnerable populations.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study

Background

School closures have been enacted as a measure of mitigation during the ongoing COVID-19 pandemic. It has been shown that school closures could cause absenteeism amongst healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.

Methods

We provide national- and county-level simulations of school closures and unmet child care needs across the United States. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors.

Results

At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.5% to 8.6%, and the effectiveness of school closures to range from 3.2% (R0 = 4) to 7.2% (R0 = 2) reduction in fewer ICU beds at peak demand. At the county-level, we find substantial variations of projected unmet child care needs and school closure effects, ranging from 1.9% to 18.3% of healthcare worker households and 5.7% to 8.8% reduction in fewer ICU beds at peak demand (R0 = 2). We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p < 0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 71.1% to 98.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures.

Conclusions

School closures are projected to reduce peak ICU bed demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible tradeoff between school closures and healthcare worker absenteeism.

Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial

Precision medicine research designed to reduce health disparities often involves studying multi-level datasets to understand how diseases manifest disproportionately in one group over another, and how scarce health care resources can be directed precisely to those most at risk for disease. In this article, we provide a structured tutorial for medical and public health researchers on the application of machine learning methods to conduct precision medicine research designed to reduce health disparities. We review key terms and concepts for understanding machine learning papers, including supervised and unsupervised learning, regularization, cross-validation, bagging, and boosting. Metrics are reviewed for evaluating machine learners and major families of learning approaches, including tree-based learning, deep learning, and ensemble learning. We highlight the advantages and disadvantages of different learning approaches, describe strategies for interpreting "black box" models, and demonstrate the application of common methods in an example dataset with open-source statistical code in R.

Mortality associated with alternative policy options for primary care and the Mais Médicos (More Doctors) Program in Brazil: forecasting future scenarios

Objective.

To forecast the impact of alternative scenarios of coverage changes in Brazil’s Family Health Strategy (Estratégia Saúde da Família) (ESF)—due to fiscal austerity measures and to the end of the Mais Médicos (More Doctors) Program (PMM)—on overall under-5 mortality rates (U5MRs) and under-70 mortality rates (U70MRs) from ambulatory care sensitive conditions (ACSCs) up through 2030.

Methods.

A synthetic cohort of 5 507 Brazilian municipalities was created for the period 2017-2030. A municipal-level microsimulation model was developed and validated using longitudinal data. Reductions in ESF coverage, and its effects on U5MRs and U70MRs from ACSCs, were forecast based on two probable austerity scenarios, as compared to the maintenance of current ESF coverage. Fixed effects longitudinal regression models were employed to account for secular trends, demographic and socioeconomic changes, variables related to health care, and program duration effects.

Results.

In comparison to maintaining stable ESF coverage, with the decrease in ESF coverage due to austerity measures and PMM termination, the mean U5MR and U70MR would be 13.2% and 8.6% higher, respectively, in 2030. The end of PMM would be responsible for a mean U5MR from ACSCs that is 4.3% higher and a U70MR from ACSCs that is 2.8% higher in 2030. The reduction of PMM coverage due only to the withdrawal of Cuban doctors who have been working in PMM would alone be responsible for a U5MR that is 3.2% higher, and a U70MR that is 2.0% higher in 2030.

Conclusions.

Reductions in primary health care coverage due to austerity measures and the end of the PMM could be responsible for many avoidable adult and child deaths in coming years in Brazil.

Utilization and Cost of an Employer-Sponsored Comprehensive Primary Care Delivery Model

IMPORTANCE

Primary care is increasingly delivered at or near workplaces, yet utilization and cost of employer-sponsored primary care services remain unknown.

OBJECTIVE

To compare the health care utilization and cost of an employer-sponsored on-site, near-site, and virtual comprehensive primary care service delivery model with those of traditional community-based primary care.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cohort study of 23 518 commercially insured employees and dependents of an engineering and manufacturing firm headquartered in southern California was performed from January 1, 2016, to July 1, 2019. A subset of the population with most (≥50%) primary care visits through employer-sponsored on-site, near-site, or virtual care clinics was matched to a subset not having most such visits through the employer-sponsored clinics using propensity score matching (n = 1983 each). In sensitivity analyses, employees were matched to dependents at neighboring firms that lacked access to the employer-sponsored primary care delivery model (additional n = 1680).

EXPOSURES

Integrated primary care, mental health, and physical therapy delivered through on-site, near-site, and virtual clinics.

MAIN OUTCOMES AND MEASURES

Utilization (inpatient, outpatient, emergency department, pharmaceutical, radiology, and laboratory visits per 1000 member-months) and spending (2019 costs per member per month in US dollars) by service type.

RESULTS

A total of 23 518 individuals (mean [SD] age, 27 [15] years; 14 604 [62.1%] male) were included in the full population sample and had been enrolled in the employer-sponsored health plan for a mean of 29 months (interquartile range, 14-48 months). Of eligible members, 5292 (22.5%) used the employer-sponsored services, with 2305 (9.8%) using them for most of their primary care. The mean (SD) cost of employer-sponsored service delivery was $87 ($32) per member month. Among the matched populations (mean [SD] age, 31 [11] years; 3349 [84.5%] male) of primary users vs control individuals, total spending was 45% lower per member per month (95% CI, 35%-55%; cost difference, -$167 per member per month; 95% CI, -$204 to -$130; P < .001) among users after adjustment. The lower spending was associated with lower spending on non-primary care services, such as emergency department (-33%; 95% CI, -44% to -22%) and hospital visits (-16%; 95% CI, -22% to -10%), despite higher spending on primary care (109%; 95% CI, 102%-116%) and mental health (20%; 95% CI, 13%-27%).

CONCLUSIONS AND RELEVANCE

The findings suggest that individuals who used the models' services for most of their primary care had lower total spending despite higher primary care spending, which may be associated with self-selection of lower-risk persons to the employer-sponsored services and/or with the use of comprehensive primary care.

Dental Practice Integration into Primary Care: A Microsimulation of Financial Implications for Practices

Given the widespread lack of access to dental care for many vulnerable Americans, there is a growing realization that integrating dental and primary care may provide comprehensive care. We sought to model the financial impact of integrating dental care provision into a primary care practice. A microsimulation model was used to estimate changes in net revenue per practice by simulating patient visits to a primary dental practice within primary care practices, utilizing national survey and un-identified claims data from a nationwide health insurance plan. The impact of potential changes in utilization rates and payer distributions and hiring additional staff was also evaluated. When dental care services were provided in the primary care setting, annual net revenue changes per practice were −$92,053 (95% CI: −93,054, −91,052) in the first year and $104,626 (95% CI: 103,315, 105,316) in subsequent years. Net revenue per annum after the first year of integration remained positive as long as the overall utilization rates decreased by less than 25%. In settings with a high proportion of publicly insured patients, the net revenue change decreased but was still positive. Integrating primary dental and primary care providers would be financially viable, but this viability depends on demands of dental utilization and payer distributions.