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An increase in predominant breastfeeding duration was positively and significantly associated with intelligence in school‐aged children. The most significant increase in intelligence was found among those who were predominantly breastfed for 4–6 months compared with less than 1 month. The association was stronger among children with low socioeconomic status. Hence, increased breastfeeding duration may reduce the intelligence gap between low versus high socioeconomic‐status children. Selection bias between breastfed and nonbreastfed children should be considered in future analyses in this area.

IntroductionOne-size-fits-all policies are not always optimal. Stratified decision-making is only possible when the characteristics used to define subgroups can be identified for stratum-specific predictions of outcomes. Conditioning decisions on characteristics that are not readily known may require information (diagnostic, prognostic, predictive) to be derived, the value of which needs to be assessed to support personalized strategies.MethodsA general framework was developed to show how personalized policies can be accountably informed by characterizing uncertainty, heterogeneity, and bias in evidence. In the framework, observed heterogeneity was disentangled from random variability by conditioning the value of the model input parameters on a set of prognostic or predictive variables, while unobserved heterogeneity was quantified as the systematic variability that cannot be explained given current information. Value of information analysis was used to quantify the value of additional information for resolving decision uncertainty in model input parameters and to identify individual- or subgroup-level attributes that contribute to the degree of heterogeneity.ResultsDecision-making based on average cost effectiveness fails to account for the role that sources of outcome variability play in guiding nuanced decision-making. Conditioning on a set of known covariates to reflect observable heterogeneity may be extended to conditioning on the latent random variable for unobservable covariates to quantify unobservable heterogeneity. Quantifying the potential value of research to inform subgroup- or individual-level attributes may be used to direct further research toward the attributes expected to be of most interest because they drive the value of individualized decisions—the expected value of sample information for attributes.ConclusionsTwo distinct, but interrelated concepts for assessing the value of stratified decision-making are important: (i) the value of heterogeneity; and (ii) the value of further research to inform both heterogeneous factors and to reduce decision uncertainty in precision medicine. Assessing the value of unexplained heterogeneity and bias can be central to supporting the value of personalized intervention strategies in health technology assessment.

Background To determine the net cost of non-drug interventions that maintain or improve a person with dementia’s physical function and/or reduce challenging behaviors. Cost data are needed to inform the adoption of non-drug interventions in health systems and the development of policies to incentivize their use. Methods We modified a person-level microsimulation to model the cost of four non-drug interventions relative to usual care: Collaborative Care, Care of Persons with Dementia in their Environments (COPE), Tailored Activity Program (TAP), and Skills2Care. We also conducted a value of information analysis to quantify the optimal sample size of conducting a new randomized trial that would reduce uncertainty on the cost savings of each intervention from a societal perspective. Finally, we conducted sensitivity analyses. Results Collaborative Care, TAP and COPE were cost savings compared to usual care (-$572, -$1,816, and -$5,262, respectively). Skills2Care results in a $89 net increase in cost compared to usual care. The value of information analysis identified the optimal sample size of a potential future study: Skills2Care (optimal n  = 8,560), TAP (optimal n  = 5,650), COPE (optimal n  = 3,910) and Collaborative Care (optimal n  = 3,630). In one-way sensitivity analyses, when we applied a pessimistic assumption for the treatment effect, COPE and TAP were still cost saving, while Collaborative Care cost more than usual care. Conclusions did not materially change in sensitivity analyses that varied treatment cost. Conclusions Non-drug dementia care interventions that maintain or improve a person with dementia’s function and/or reduce challenging behaviors present a viable clinical / economic model of care for health systems. Key points Three (Collaborative Care, Care of Persons with Dementia in their Environments, and Tailored Activity Program) of four (Skills2Care) non-drug dementia interventions that maintain or improve a person with dementia’s physical function and/or reduce challenging behaviors are cost savings compared to usual care. The National Institutes of Health is investing in pragmatic trials to test the effectiveness of dementia care interventions. Using a value of information analysis, we found that large sample sizes (between 3,630 and 8,560 people) are needed to reduce uncertainty as to whether non-drug dementia care interventions are cost savings. This finding reinforces the need for pragmatic studies and also highlights the value of microsimulation methodologies. Findings demonstrate the financial benefit of selected evidence-based caregiver support programs for health systems engaged in the GUIDE model or operating in a capitated payment system.

We describe associations of pretreatment drug resistance (PDR) with clinical outcomes such as remaining in care, loss to follow-up (LTFU), viral suppression, and death in Mexico, in real-life clinical settings. We analyzed clinical outcomes after a two-year follow up period in participants of a large 2017–2018 nationally representative PDR survey cross-referenced with information of the national ministry of health HIV database. Participants were stratified according to prior ART exposure and presence of efavirenz/nevirapine PDR. Using a Fine-Gray model, we evaluated virological suppression among resistant patients, in a context of competing risk with lost to follow-up and death. A total of 1823 participants were followed-up by a median of 1.88 years (Interquartile Range (IQR): 1.59–2.02): 20 (1%) were classified as experienced + resistant; 165 (9%) naïve + resistant; 211 (11%) experienced + non-resistant; and 1427 (78%) as naïve + non-resistant. Being ART-experienced was associated with a lower probability of remaining in care (adjusted Hazard Ratio(aHR) = 0.68, 0.53–0.86, for the non-resistant group and aHR = 0.37, 0.17–0.84, for the resistant group, compared to the naïve + non-resistant group). Heterosexual cisgender women compared to men who have sex with men [MSM], had a lower viral suppression (aHR = 0.84, 0.70–1.01, p = 0.06) ART-experienced persons with NNRTI-PDR showed the worst clinical outcomes. This group was enriched with women and persons with lower education and unemployed, which suggests higher levels of social vulnerability.

Background. Mexico City Metropolitan Area (MCMA) has the largest number of COVID-19 (coronavirus disease 2019) cases in Mexico and is at risk of exceeding its hospital capacity in early 2021. Methods. We used the Stanford-CIDE Coronavirus Simulation Model (SC-COSMO), a dynamic transmission model of COVID-19, to evaluate the effect of policies considering increased contacts during the end-of-year holidays, intensification of physical distancing, and school reopening on projected confirmed cases and deaths, hospital demand, and hospital capacity exceedance. Model parameters were derived from primary data, literature, and calibrated. Results. Following high levels of holiday contacts even with no in-person schooling, MCMA will have 0.9 million (95% prediction interval 0.3–1.6) additional COVID-19 cases between December 7, 2020, and March 7, 2021, and hospitalizations will peak at 26,000 (8,300–54,500) on January 25, 2021, with a 97% chance of exceeding COVID-19-specific capacity (9,667 beds). If MCMA were to control holiday contacts, the city could reopen in-person schools, provided they increase physical distancing with 0.5 million (0.2–0.9) additional cases and hospitalizations peaking at 12,000 (3,700–27,000) on January 19, 2021 (60% chance of exceedance). Conclusion. MCMA must increase COVID-19 hospital capacity under all scenarios considered. MCMA’s ability to reopen schools in early 2021 depends on sustaining physical distancing and on controlling contacts during the end-of-year holiday.

Purpose. In 2018, the US Preventive Services Task Force (USPSTF) endorsed three strategies for cervical cancer screening in women ages 30 to 65: cytology every 3 years, testing for high-risk types of human papillomavirus (hrHPV) every 5 years, and cytology plus hrHPV testing (co-testing) every 5 years. It further recommended that women discuss with health care providers which testing strategy is best for them. To inform such discussions, we used decision analysis to estimate outcomes of screening strategies recommended for women at age 30. Methods. We constructed a Markov decision model using estimates of the natural history of HPV and cervical neoplasia. We evaluated the three USPSTF-endorsed strategies, hrHPV testing every 3 years and no screening. Outcomes included colposcopies with biopsy, false-positive testing (a colposcopy in which no cervical intraepithelial neoplasia grade 2 or worse was found), treatments, cancers, and cancer mortality expressed per 10,000 women over a shorter-than-lifetime horizon (15-year). Results. All strategies resulted in substantially lower cancer and cancer death rates compared with no screening. Strategies with the lowest likelihood of cancer and cancer death generally had higher likelihood of colposcopy and false-positive testing. Conclusions. The screening strategies we evaluated involved tradeoffs in terms of benefits and harms. Because individual women may place different weights on these projected outcomes, the optimal choice for each woman may best be discerned through shared decision making.

Low adherence to preventative medications against life-long health conditions is a major contributor to global morbidity and mortality. We implemented a pilot randomized controlled trial in Mexico to measure the extent to which conditional economic incentives help male sex workers increase their adherence to pre-exposure prophylaxis (PrEP) for HIV prevention. We followed n  = 110 male sex workers over 6 months. At each quarterly visit (at months 0, 3, and 6), all workers received a $10 transport reimbursement, a free 3-month PrEP supply, and completed socio-behavioral surveys. The primary outcome was an objective biomarker of medication adherence based on tenofovir (TFV) drug concentration levels in hair collected at each visit. Individuals randomized to the intervention received incentives based on a grading system as a function of PrEP adherence: those with high (> 0.043 ng/mg TFV concentration), medium (0.011 to 0.042 ng/mg), or low (< 0.011 ng/mg) adherence received $20, $10, or $0, respectively. Six-month pooled effects of incentives on PrEP adherence were analyzed using population-averaged gamma generalized estimating equation models. We estimated heterogeneous treatment effects by sex worker characteristics. The incentive intervention led to a 28.7% increase in hair antiretroviral concentration levels over 6 months consistent with increased PrEP adherence ( p  = 0.05). The effect of incentives on PrEP adherence was greater for male sex workers who were street-based (vs. internet) workers ( p  < 0.10). These pilot findings suggest that modest conditional economic incentives could be effective, at scale, for improving PrEP adherence among male sex workers, and should be tested in larger implementation trials. ClinicalTrials.gov Identifier: NCT03674983.

Objectives The aim of this study was to quantify the value of conducting additional research and reducing uncertainty regarding the cost effectiveness of allopurinol and febuxostat for the management of gout. Methods We used a previously developed Markov model that evaluated the cost effectiveness of nine urate-lowering strategies: no treatment, allopurinol-only fixed dose (300 mg), allopurinol-only dose escalation (up to 800 mg), febuxostat-only fixed dose (80 mg), febuxostat-only dose escalation (up to 120 mg), allopurinol–febuxostat sequential therapy fixed dose, allopurinol–febuxostat sequential therapy dose escalation, febuxostat–allopurinol sequential therapy fixed dose, and febuxostat–allopurinol sequential therapy dose escalation. Each strategy was evaluated over the lifetime of a hypothetical gout patient. We calculated population expected value of perfect information (EVPI). We used a linear regression meta-modeling approach to calculate population expected value of partial perfect information (EVPPI), and a Gaussian approximation to calculate the population expected value of sample information for parameters (EVSI) and the expected net benefit of sampling (ENBS) for four potential study designs: (1) an allopurinol efficacy trial; (2) a febuxostat efficacy trial; (3) a prospective observational study evaluating health utilities; and (4) a comprehensive study evaluating the efficacy of allopurinol and febuxostat and health utilities. A 5-year decision time horizon was used in the base-case analysis. Results EVPI varied by a decision maker’s willingness-to-pay (WTP) per quality-adjusted life-year (QALY) and was $US900 million for WTP of $US60,000 per QALY. Population EVPPI was highest across all WTP values for study design #4. For study design #4 and a WTP of $US60,000 per QALY, the optimal sample size was 735 patients per study arm. Conclusions Future studies are needed to evaluate the effectiveness of allopurinol and febuxostat dose escalation.

BackgroundCorrectional institutions nationwide are seeking to mitigate COVID-19-related risks.ObjectiveTo quantify changes to California’s prison population since the pandemic began and identify risk factors for COVID-19 infection.DesignFor California state prisons (March 1–October 10, 2020), we described residents’ demographic characteristics, health status, COVID-19 risk scores, room occupancy, and labor participation. We used Cox proportional hazard models to estimate the association between rates of COVID-19 infection and room occupancy and out-of-room labor, respectively.ParticipantsResidents of California state prisons.Main MeasuresChanges in the incarcerated population’s size, composition, housing, and activities. For the risk factor analysis, the exposure variables were room type (cells vs. dormitories) and labor participation (any room occupant participating in the prior 2 weeks) and the outcome variable was incident COVID-19 case rates.Key ResultsThe incarcerated population decreased 19.1% (119,401 to 96,623) during the study period. On October 10, 2020, 11.5% of residents were aged ≥60, 18.3% had high COVID-19 risk scores, 31.0% participated in out-of-room labor, and 14.8% lived in rooms with ≥10 occupants. Nearly 40% of residents with high COVID-19 risk scores lived in dormitories. In 9 prisons with major outbreaks (6,928 rooms; 21,750 residents), dormitory residents had higher infection rates than cell residents (adjusted hazard ratio [AHR], 2.51 95% CI, 2.25–2.80) and residents of rooms with labor participation had higher rates than residents of other rooms (AHR, 1.56; 95% CI, 1.39–1.74).ConclusionDespite reductions in room occupancy and mixing, California prisons still house many medically vulnerable residents in risky settings. Reducing risks further requires a combination of strategies, including rehousing, decarceration, and vaccination.

Background Clinical trials often report intervention efficacy in terms of the reduction in all-cause mortality between the treatment and control arms (i.e., an overall hazard ratio [oHR]) instead of the reduction in disease-specific mortality (i.e., a disease-specific hazard ratio [dsHR]). Using oHR to reduce all-cause mortality beyond the time horizon of the trial may introduce bias if the relative proportion of other-cause mortality increases with age. We sought to quantify this oHR extrapolation bias and propose a new approach to overcome this bias. Methods We simulated a hypothetical cohort of patients with a generic disease that increased background mortality by a constant additive disease-specific rate. We quantified the bias in terms of the percentage change in life expectancy gains with the intervention under an oHR compared with a dsHR approach as a function of the cohort start age, the disease-specific mortality rate, dsHR, and the duration of the intervention’s effect. We then quantified the bias in a cost-effectiveness analysis (CEA) of implantable cardioverter-defibrillators based on efficacy estimates from a clinical trial. Results For a cohort of 50-year-old patients with a disease-specific mortality of 0.05, a dsHR of 0.5, a calculated oHR of 0.55, and a lifetime duration of effect, the bias was 28%. We varied these key parameters over wide ranges and the resulting bias ranged between 3 and 140%. In the CEA, the use of oHR as the intervention’s effectiveness overestimated quality-adjusted life expectancy by 9% and costs by 3%, biasing the incremental cost-effectiveness ratio by − 6%. Conclusions The use of an oHR approach to model the intervention’s effectiveness beyond the time horizon of the trial overestimates its benefits. In CEAs, this bias could decrease the cost of a QALY, overestimating interventions’ cost effectiveness.