Population-level Interventions Research Articles

Effective Community-Based Interventions for the Prevention and Management of Heat-Related Illnesses: A Scoping Review.

Background: Extreme temperatures have negative consequences on the environment, ecosystem, and human health. With recent increases in global temperatures, there has been a rise in the burden of heat-related illnesses, with a disproportionate impact on low- and middle-income countries. Effective population-level interventions are critical to a successful public health response. Objective: This scoping review aims to summarize the evidence on the effectiveness of population-level heat-related interventions and serve as a potential guide to the implementation of these interventions. Methods: Studies that evaluated the effectiveness of community-based interventions to mitigate or reduce the impact of extreme heat on heat-related mortality and morbidity were sought by searching four electronic databases. Studies published in the English language and those that had quantifiable, measurable mortality, morbidity or knowledge score outcomes were included. Results: The initial electronic search yielded 2324 articles, and 17 studies were included. Fourteen studies were based in high-income countries (HICs) (Europe, US, Canada) and discussed multiple versions of (1) heat action plans, which included but were not limited to establishing a heat monitoring system, informative campaigns, the mobilization of health care professionals, volunteers, social workers and trained caregivers in the surveillance and management of individuals with known vulnerabilities, or stand-alone (2) education and awareness campaigns. Multi-pronged heat action plans were highly effective in reducing heat-related mortality and morbidity, especially among vulnerable populations such as the elderly and those with chronic conditions. Conclusions: The heat action plans covered in these studies have shown promising results in reducing heat-related mortality and morbidity and have included instituting early warning systems, building local capacity to identify, prevent or treat and manage heat-related illnesses, and disseminating information. Nevertheless, they need to be cost-effective, easy to maintain, ideally should not rely on a mass effort from people and should be specifically structured to meet the local needs and resources of the community.

Synthetic Control Methods for the Evaluation of Single-Unit Interventions in Epidemiology: A Tutorial.

Evaluating the impacts of population-level interventions (e.g., changes to state legislation) can be challenging as conducting randomized experiments is often impractical and inappropriate, especially in settings where the intervention is implemented in a single, aggregate unit (e.g., a country or state). A common nonrandomized alternative is to compare outcomes in the treated unit(s) with unexposed controls both before and after the intervention. However, the validity of these designs depends on the use of controls that closely resemble the treated unit on before-intervention characteristics and trends on the outcome, and suitable controls may be difficult to find because the number of potential control regions is typically limited. The synthetic control method provides a potential solution to these problems by using a data-driven algorithm to identify an optimal weighted control unit—a “synthetic control”—based on data from before the intervention from available control units. While popular in the social sciences, the method has not garnered as much attention in health research, perhaps due to a lack of accessible texts aimed at health researchers. We address this gap by providing a comprehensive, nontechnical tutorial on the synthetic control method, using a worked example evaluating Florida’s “stand your ground” law to illustrate methodological and practical considerations.

Genetic surveillance for monitoring the impact of drug use on Plasmodium falciparum populations

The use of antimalarial drugs is an effective strategy in the fight against malaria. However, selection of drug resistant parasites is a constant threat to the continued use of this approach. Antimalarial drugs are used not only to treat infections but also as part of population-level strategies to reduce malaria transmission toward elimination. While there is strong evidence that the ongoing use of antimalarial drugs increases the risk of the emergence and spread of drug-resistant parasites, it is less clear how population-level use of drug-based interventions like seasonal malaria chemoprevention (SMC) or mass drug administration (MDA) may contribute to drug resistance or loss of drug efficacy. Critical to sustained use of drug-based strategies for reducing the burden of malaria is the surveillance of population-level signals related to transmission reduction and resistance selection. Here we focus on Plasmodium falciparum and discuss the genetic signatures of a parasite population that are correlated with changes in transmission and related to drug pressure and resistance as a result of drug use. We review the evidence for MDA and SMC contributing to malaria burden reduction and drug resistance selection and examine the use and impact of these interventions in Senegal. Throughout we consider best strategies for ongoing surveillance of both population and resistance signals in the context of different parasite population parameters. Finally, we propose a roadmap for ongoing surveillance during population-level drug-based interventions to reduce the global malaria burden.

A latent growth curve modelling approach to seasonal and spatial dynamics of food security heterogeneities in rural Lake Naivasha Basin, Kenya

The increasing complexity of food insecurity, malnutrition, and chronic poverty faced by Sub-Saharan Africa warrants urgent categorisation and tracking of household food security along both temporal and spatial dimensions. This will help to effectively target, monitor and evaluate population-level programs and specific interventions aimed at addressing food insecurity. Traditional longitudinal analysis does not address the dynamics of inter- and intrahousehold heterogeneities within the seasonal and spatial context of household-level food security. This study is the first to overcome such limitations by adopting a multi-group piecewise latent growth curve model in the analysis of the food security situation in a statistically representative sample of 601 households involved in subsistence and cut-flower commercial agriculture, around Lake Naivasha. We considered food security as a latent concept, which manifests as food security outcomes in our primary longitudinal dataset from March 2018 to January 2019. Our analysis highlights the temporal and spatial dynamics of food security and advances new evidence on inter- and intrahousehold heterogeneities in food security across different seasons for the subsistence and commercial farming clusters. These heterogeneities were demonstrated primarily during the hunger season from March to June, and persisted in both the clusters and across months, albeit with different intensities. Moreover, our results indicate the importance of commercial agriculture in achieving food security in the hunger season. Our study suggests the need of a multidisciplinary approach to food security and the introduction of well-coordinated interventions for the development of subsistence and commercial agriculture considering the seasonal and cluster-level specificities.

Future Directions in Research to Improve Care Transitions From Hospital Discharge.

Future Directions in Research to Improve Care Transitions From Hospital Discharge.

Is There a Relationship between County-Level Food Insecurity Rates and Breast Cancer Stage at Diagnosis?

The relationship between county food insecurity (FI) rate and breast cancer stage at diagnosis is not clear. Using 2010-2016 Surveillance Epidemiology and End Results (SEER) and Feeding America data we analyzed the association between county FI rate in quartiles (low, medium, high, very high) and breast cancer stage at diagnosis among adult females (≥18 years). We also analyzed the effect of insurance status and county poverty level on this relationship, and whether this relationship varies among non-elderly (<65 years) and elderly (≥ 65 years) individuals. Bivariate and multivariable multilevel logistic regression were used for analyses. Bivariate analysis showed increased likelihood of late-stage breast cancer with increasing county FI rate. This relationship persisted after adjusting for insurance status but was no longer significant after adjusting for county-level poverty rate. There was a statistically significant association between counties with very high food insecurity rates and late-stage breast cancer diagnosis (OR = 1.07; 95% CI = 1.00, 1.14) among the elderly population. Very high county food insecurity rate was associated with late-stage breast cancer among elderly women. Population-level interventions focused on counties with very high food insecurity rates could reduce disparities in stage at breast cancer diagnosis among elderly women.

Trends in the diabetes incidence and mortality in India from 1990 to 2019: a joinpoint and age-period-cohort analysis.

Globally, a metabolic disorder like Diabetes is considered as one of the largest global health issues, as it accounts for the majority of the disease burden and happens to be one of the leading causes of mortality as well as reduced life expectancy across the world. As in 2019, India is home to the second-largest number (77million) of Diabetic adults and the number of people affected has been increasing rapidly over the years. Termed as "the diabetes capital of the world," with every fifth diabetic in the world being an Indian, there is an urgent need to address many critically significant challenges posed by Diabetes in India, like, increasing prevalence among young people in urban areas, less awareness among people, high cost of disease management, limited healthcare facilities, suboptimal diabetes control etc. In Indian context, not enough attempts have been made to observe and understand the long-term pattern of diabetes incidence and mortality. This study aims to provide deep insights into the recent trends of diabetes incidence and mortality in India from 1990 to 2019. This is an observational study based on the most recent data from the Global Burden of Disease (GBD) Study 2019. We extracted numbers, age-specific and age-standardized incidence and mortality rates of diabetes (from 1990 to 2019) from the Global Health Data Exchange. The average annual percentage changes in incidence and mortality were analysed by joinpoint regression analysis; the net age, period, and cohort effects on the incidence and mortality were estimated by age-period-cohort analysis. During the study period, age-standardized incidence and mortality rates of diabetes in India experienced an upsurge in numbers, the incidence rate increasedfrom 199.14 to 317.02, and consequently, mortality increased from 22.30 to 27.35 per 100,000 population. The joinpoint regression analysis showed that the age-standardized incidence significantly rose by 1.63 % (95 % CI: 1.57 %, 1.69 %) in Indian males and 1.56 % in Indian females (95 % CI: 1.49 %, 1.63 %) from 1990 to 2019. On the other hand, the age-standardized mortality rates rose by 0.77 % (95 % CI: 0.24 %, 1.31 %) in Indian males and 0.57 % (95 % CI: -0.54 %, 1.70 %) in Indian females. For age-specific rates, incidence increased in most age groups, with exception of age groups 5-9, 70-74, 75-79 and 80-84 in male, and age groups 5-9, 75-79 and 80-84 in female. Mortality in male saw a decreasing trend till age group 20-24, whereas in female, the rate decreased till age group 35-39. The age effect on incidence showed no obvious changes with advancing age, but the mortality significantly increased with advancing age; period effect showed that both incidence and mortality increased with advancing time period; cohort effect on diabetes incidence and mortality decreased from earlier birth cohorts to more recent birth cohorts, while incidence showed no material changes from 1975 to 1979 to 2000-2004 birth cohort. Mortality of diabetes decreased in younger age groups but increased in older age groups; however, Incidence increased in most age groups for both male and female. The net age or period effect showed an unfavourable trend while the net cohort effect presented a favourable trend. Aging was likely to drive a continued increase in the mortality of diabetes. Timely population-level interventions aiming for health education, lifestyle modification with special emphasis on the promotion of physical activity and healthy diet should be conducted, especially for male and earlier birth cohorts at high risk of diabetes.

Persistence of Disparities Among Racially/Ethnically Marginalized Groups in the Coronavirus Disease 2019 Pandemic Regardless of Statewide Shelter-in-Place Policies: An Analysis From Northern California.

To measure disparities in coronavirus disease 2019 (COVID-19) hospitalization and intensive care unit (ICU) transfer among racially/ethnically marginalized groups before and after implementation of the California statewide shelter-in-place (SIP) policy, we conducted a retrospective cohort study within a health-care system in California. COVID-19 patients diagnosed from January 1, 2020, to August 31, 2020, were identified from electronic health records. We examined hospitalizations and ICU transfers by race/ethnicity and pandemic period using logistic regression. Among 16,520 people with COVID-19 (mean age = 46.6 (standard deviation, 18.4) years; 54.2% women), during the post-SIP period, patients were on average younger and a larger proportion were Hispanic. In adjusted models, odds of hospitalization were 20% lower post-SIP as compared with the SIP period, yet all non-White groups had higher odds (odds ratios=1.6-2.1) than non-Hispanic White individuals, regardless of period. Among hospitalized patients, odds of ICU transfer were 33% lower post-SIP than during SIP. Hispanic and Asian patients had higher odds than non-Hispanics. Disparities in hospitalization persisted and ICU risk became more pronounced for Asian and Hispanic patients post-SIP. Policy-makers should consider ways to proactively address racial/ethnic inequities in risk when considering future population-level policy interventions for public health crises.

Abstract 857: Evaluating the relationship between arsenic exposure and cancer risk in Canada

Abstract Background: Arsenic is an established carcinogen for cancers of the skin, bladder and lungs; it is also the most prevalent environmental toxin, resulting in all Canadians being exposed to some extent. In a recent prospective study of a low-exposure population, our team found that women in the highest quartile of circulating arsenic had a significant 13-fold increased risk of developing breast cancer, compared to the lowest quartile. This suggests that even at low-doses, arsenic may be an important risk factor for breast, and potentially other cancers not previously recognized. To date, the sources of arsenic in Canada, a low-exposure population, are unclear. Objectives: To describe the status of arsenic exposure, identify the key predictors of arsenic status, and evaluate the relationship between arsenic exposure and cancer risk within the Canadian population. Methods: Using data from Cycles 1-5 (2007-2017) of the Canadian Health Measures Survey (CHMS), exposure data (e.g., diet, water), demographic information, and urinary arsenic biomarkers will be analyzed. The CHMS is an ongoing, nationally representative survey, collecting objective health measures and food intake information from participants in biennial cycles. Data will be described using weighted percentages or means, and arsenic levels (total and speciated urinary arsenic metabolite concentrations) will be reported by percentile. Weighted multivariate regression models will be used to identify predictors of total urinary arsenic. Linkage to the Canadian Cancer Registry will be performed to ascertain incident cancers, and Cox proportional hazards regression used to estimate the relationship between arsenic status and cancer risk (total and site-specific). Results: This analysis included 15,700 individuals, aged 18-80. The median total urinary arsenic level was 7.5 ug/g of creatinine. Significant predictors of total urinary arsenic levels were found to be weekly intake of fish and seafood (β= 0.3, 95%CI 0.27,0.33, P &amp;lt; 0.001), grains (β=-0.02, 95%CI -0.02, -0.01, P&amp;lt; 0.001) and consumption of well and/or cistern water (β = 0.13, 95%CI 0.04, 0.22, P=0.005). Linkage and data analysis are ongoing. Impact: This work is the first to ascertain national exposure estimates to an environmental carcinogen. Fish, seafood, grains, and consumption of well and/or cistern water represent significant predictors of total urinary arsenic levels in Canada. Ultimately, this information is needed to develop critical population-level intervention strategies to reduce the Canadian, and global burden of cancer. Citation Format: Katherine Pullella, Shelley A. Harris, John R. McLaughlin, Jan Lubinski, Steven A. Narod, Joanne Kotsopoulos. Evaluating the relationship between arsenic exposure and cancer risk in Canada [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 857.

A (Flexible) Synthetic Control Method for Count Data and Other Nonnegative Outcomes.

The synthetic control method is a covariate balancing method that exploits data from untreated regions to construct a synthetic control that approximates a single, aggregate treatment unit on a time series of preintervention outcomes and covariates. The method is increasingly being used to evaluate population-level interventions in epidemiology. Although the original version can be used with bounded outcomes, it imposes strong constraints on the balancing weights to ensure that the counterfactuals are based solely on interpolation. This feature, while attractive from a causal inference perspective, is sometimes too conservative and can lead to unnecessary bias due to poor covariate balance. Alternatives exist that allow for extrapolation to improve balance but existing procedures may produce negative estimates of the counterfactual outcomes and are therefore inappropriate for count data. We propose an alternative way to allow for extrapolation, although ensuring that the estimated counterfactuals remain nonnegative. Following a related proposal, we add a penalty to the balancing procedure that favors interpolation over extrapolation whenever possible. As we demonstrate theoretically and using empirical examples, our proposal can serve as a useful alternative when existing approaches yield demonstrably poor or unrealistic counterfactuals. Finally, we provide functions to implement the method in R.

Risk Factors for Premature Myocardial Infarction: A Systematic Review and Meta-analysis of 77 Studies.

ObjectiveTo evaluate the magnitude of the association between risk factors and premature myocardial infarction (MI) (men aged 18-55 years; women aged 18-65 years).Patients and MethodsWe searched MEDLINE and other databases from inception through April 30, 2020, as well as bibliography of articles selected for data extraction. We selected observational studies reporting the magnitude of the association of at least 1 risk factor (demographic characteristics, lifestyle factors, clinical risk factors, or biomarkers) with premature MI and a control group. Pooled risk estimates (random effects) from all studies unadjusted and adjusted for risk factors were reported as summary odds ratios (ORs) with 95% CIs.ResultsFrom 35,320 articles of 12.7 million participants, we extracted data on 19 risk factors from 77 studies across 58 countries. Men had a higher risk of premature MI (OR, 2.39; 95% CI, 1.71 to 3.35) than did women. Family history of cardiac disease was associated with a higher risk of premature MI (OR, 2.67; 95% CI, 2.29 to 3.27). Major modifiable risk factors associated with higher risk were current smoking (OR, 4.34; 95% CI, 3.68 to 5.12 vs no/former), diabetes mellitus (OR, 3.54; 95% CI, 2.69 to 4.65), dyslipidemia (OR, 2.94; 95% CI, 1.76 to 4.91), and hypertension (OR, 2.85; 95% CI, 2.48 to 3.27). Higher body mass index carried higher risk (OR, 1.46; 95% CI, 1.24 to 1.71 for ≥25 kg/m2 vs <25 kg/m2). Biomarkers associated with 2- to 3-fold higher risk were total cholesterol levels greater than 200 mg/dL, triglyceride levels higher than 150 mg/dL, and high-density lipoprotein cholesterol levels less than 60 mg/dL (to convert to mmol/L, multiply by 0.0259).ConclusionMajor risk factors for premature MI are mostly amenable to patient, population, and policy level interventions. Mild elevations in body mass index and triglyceride levels were associated with higher risk, which has implications for the growing worldwide epidemic of cardiometabolic diseases.

Identifying Predictors of University Students' Wellbeing during the COVID-19 Pandemic-A Data-Driven Approach.

Background: The COVID-19 pandemic has posed risks to public mental health worldwide. University students, who are already recognised as a vulnerable population, are at elevated risk of mental health issues given COVID-19-related disruptions to higher education. To assist universities in effectively allocating resources to the launch of targeted, population-level interventions, the current study aimed to uncover predictors of university students’ psychological wellbeing during the pandemic via a data-driven approach. Methods: Data were collected from 3973 Australian university students ((median age = 22, aged from 18 to 79); 70.6% female)) at five time points during 2020. Feature selection was conducted via least absolute shrinkage and selection operator (LASSO) to identify predictors from a comprehensive set of variables. Selected variables were then entered into an ordinary least squares (OLS) model to compare coefficients and assess statistical significance. Results: Six negative predictors of university students’ psychological wellbeing emerged: White/European ethnicity, restriction stress, perceived worry on mental health, dietary changes, perceived sufficiency of distancing communication, and social isolation. Physical health status, emotional support, and resilience were positively associated with students’ psychological wellbeing. Social isolation has the largest effect on students’ psychological wellbeing. Notably, age, gender, international status, and educational level did not emerge as predictors of wellbeing. Conclusion: To cost-effectively support student wellbeing through 2021 and beyond, universities should consider investing in internet- and tele- based interventions explicitly targeting perceived social isolation among students. Course-based online forums as well as internet- and tele-based logotherapy may be promising candidates for improving students’ psychological wellbeing.

A scoping review of alcohol, tobacco, and other drug use treatment interventions for sexual and gender minority populations

BackgroundAlcohol, tobacco, and other drug use are among the most prevalent and important health disparities affecting sexual and gender minority (SGM; e.g., lesbian, gay, bisexual, transgender) populations. Although numerous government agencies and health experts have called for substance use intervention studies to address these disparities, such studies continue to be relatively rare. MethodWe conducted a scoping review of prevention and drug treatment intervention studies for alcohol, tobacco, and other drug use that were conducted with SGM adults. We searched three databases to identify pertinent English-language, peer-reviewed articles published between 1985 and 2019. ResultsOur search yielded 71 articles. The majority focused on sexual minority men and studied individual or group psychotherapies for alcohol, tobacco, or methamphetamine use. ConclusionOur findings highlight the need for intervention research focused on sexual minority women and gender minority individuals and on cannabis and opioid use. There is also a need for more research that evaluates dyadic, population-level, and medication interventions.

Prevalence of coronary risk factors in contemporary practice among patients undergoing their first percutaneous coronary intervention: Implications for primary prevention.

BackgroundCigarette smoking, hypertension, dyslipidemia, diabetes, and obesity are conventional risk factors (RFs) for coronary artery disease (CAD). Population trends for these RFs have varied in recent decades. Consequently, the risk factor profile for patients presenting with a new diagnosis of CAD in contemporary practice remains unknown.ObjectivesTo examine the prevalence of RFs and their temporal trends among patients without a history of myocardial infarction or revascularization who underwent their first percutaneous coronary intervention (PCI).MethodsWe examined the prevalence and temporal trends of RFs among patients without a history of prior myocardial infarction, PCI, or coronary artery bypass graft surgery who underwent PCI at 47 non-federal hospitals in Michigan between 1/1/2010 and 3/31/2018.ResultsOf 69,571 men and 38,930 women in the study cohort, 95.5% of patients had 1 or more RFs and nearly half (55.2% of women and 48.7% of men) had ≥3 RFs. The gap in the mean age at the time of presentation between men and women narrowed as the number of RFs increased with a gap of 6 years among those with 2 RFs to <1 year among those with 5 RFs. Compared with patients without a current/recent history of smoking, those with a current/recent history of smoking presented a decade earlier (age 56.8 versus 66.9 years; p <0.0001). Compared with patients without obesity, patients with obesity presented 4.0 years earlier (age 61.4 years versus 65.4 years; p <0.0001).ConclusionsModifiable RFs are widely prevalent among patients undergoing their first PCI. Smoking and obesity are associated with an earlier age of presentation. Population-level interventions aimed at preventing obesity and smoking could significantly delay the onset of CAD and the need for PCI.

Cost-Effectiveness of Population Level and Individual Level Interventions to Combat Non-communicable Disease in Eastern Sub-Saharan Africa and South East Asia: A WHO-CHOICE Analysis.

Background: To determine the health system costs and health-related benefits of interventions for the prevention and control of non-communicable diseases (NCDs), including mental health disorders, for the purpose of identifying the most cost-effective intervention options in support of global normative guidance on the best-buy interventions for NCDs. In addition, tools are developed to allow country contextualisation of the analyses to support local priority setting exercises. Methods: This analysis follows the standard WHO-CHOICE (World Health Organization-Choosing Interventions that are Cost-Effective) approach to generalized cost-effectiveness analysis applied to two regions, Eastern sub-Saharan Africa and South-East Asia. The scope of the analysis is all NCD and mental health interventions included in WHO guidelines or guidance documents for which the health impact of the intervention is able to be identified and attributed. Costs are measured in 2010 international dollars, and benefits modelled beginning in 2010, both for a period of 100 years. Results: There are many interventions for NCD prevention and management that are highly cost-effective, generating one year of healthy life for less than Int. $100. These interventions include tobacco and alcohol control policies such as taxation, voluntary and legislative actions to reduce sodium intake, mass media campaigns for reducing physical activity, and treatment options for cardiovascular disease (CVD), cervical cancer and epilepsy. In addition a number of interventions fall just outside this range, including breast cancer, depression and chronic lung disease treatment. Conclusion: Interventions that represent good value for money, are technically feasible and are delivered for a low per-capita cost, are available to address the rapid rise in NCDs in low- and middle-income countries. This paper also describes a tool to support countries in developing NCD action plans.

A National Cost Analysis of Community Interventions to Prevent Underage Drinking and Prescription Drug Misuse

This prospective cost analysis addresses a gap in the prevention literature by providing estimates of the typical real-world costs to implement community interventions focused on preventing underage drinking and prescription drug misuse. The study uses cost data reported by more than 400 community subrecipients participating in a national cross-site evaluation of the Substance Abuse and Mental Health Services Administration's Strategic Prevention Framework Partnerships for Success grant program during 2013-2017. Community subrecipient organizations completed an annual Web-based survey to report their intervention costs. The analysis compares the relative startup and annual ongoing implementation costs of different prevention strategies and services. Partnerships for Success communities implemented a wide variety of interventions. Annual ongoing implementation was typically more costly than intervention startup. Costs were generally similar for population-level interventions, such as information dissemination and environmental strategies, and individual-level interventions, such as prevention education and positive alternative activities. However, population-level interventions reached considerably more people and consequently had much lower costs per person. Personnel contributed the most to intervention costs, followed by intervention supplies and overhead. Startup costs for initial training and costs for incentives, ongoing training, and in-kind contributions (nonlabor) during ongoing implementation were not typically reported. This study informs prevention planning by providing detailed information about the costs of classes of interventions used in communities, outside of research settings.

Implementation of a standardised and quality-assured enhanced gonococcal antimicrobial surveillance programme in accordance with WHO protocols in Kampala, Uganda

ObjectivesThe emergence of multidrug-resistant Neisseria gonorrhoeae (NG) is a major global health threat necessitating response and control measures. NG antimicrobial resistance (AMR) surveillance data from sub-Saharan countries is exceedingly limited....

Racial and Ethnic Disparities in Health Information Technology Use and Associated Trends among Individuals Living with Chronic Diseases.

Health information technology (HIT) can enhance optimal health care access and utilization among individuals living with chronic diseases. This study aimed to provide population-level information on racial/ethnic disparities in HIT use and associated trends among those living with chronic diseases. The study sample consisted of adult respondents (≥18 years) of the 2011-2018 National Health Interview Survey living with at least 1 chronic condition. Binomial regression was used to analyze the association between race/ethnicity, year, and 4 measures of HIT use for patient-provider interaction. Regression parameter estimates were used to predict the trends in probability of the outcome variables across race/ethnicity. About 73% of the study sample were non-Hispanic Whites, 15% were non-Hispanic Blacks, and 13% were Hispanics. Compared to non-Hispanic Whites, there were decreased adjusted odds of any HIT use among non-Hispanic Blacks (OR = 0.72; 95% CI = 0.67, 0.76) and Hispanics (OR = 0.78; 95% CI = 0.72, 0.84). The likelihood of any HIT use increased with increasing year (OR: 1.16; 95% CI = 1.15, 1.18). Trends in racial/ethnic disparities were wider for email communication with provider and online prescription refill compared to online scheduling of appointment. The COVID-19 pandemic has led to accelerated adoption or expansion of HIT for patient care. Limited HIT use among non-Hispanic Blacks and Hispanics could worsen the disproportionate chronic disease burden, suboptimal clinical outcomes, and preventable health care costs experienced by this subpopulation. In conclusion, there is a need for intentional and strategic population-level interventions to increase HIT adoption and use among non-Hispanic Blacks and Hispanics living with chronic diseases.

Cost-Effectiveness of Improved Hypertension Management in India through Increased Treatment Coverage and Adherence: A Mathematical Modeling Study.

Background:Despite the availability of effective and affordable treatments, only 14% of hypertensive Indians have controlled blood pressure. Increased hypertension treatment coverage (the proportion of individuals initiated on treatment) and adherence (proportion of patients taking medicines as recommended) promise population health gains. However, governments and other payers will not invest in a large-scale hypertension control program unless it is both affordable and effective.Objective:To investigate if a national hypertension control intervention implemented across the private and public sector facilities in India could save overall costs of CVD prevention and treatment.Methods:We developed a discrete-time microsimulation model to assess the cost-effectiveness of population-level hypertension control intervention in India for combinations of treatment coverage and adherence targets. Input clinical parameters specific to India were obtained from large-scale surveys such as the Global Burden of Disease as well as local clinical trials. Input hypertensive medication cost parameters were based on government contracts. The model projected antihypertensive treatment costs, avoided CVD care costs, changes in disability-adjusted life year (DALYs) and incremental cost per DALY averted (represented as incremental cost-effectiveness ratio or ICER) over 20 years.Results:Over 20 years, at 70% coverage and adherence, the hypertension control intervention would avert 1.68% DALYs and be cost-saving overall. Increasing adherence (while keeping coverage constant) resulted in greater improvement in cost savings compared to increasing coverage (while keeping adherence constant). Results were most sensitive to the cost of antihypertensive medication, but the intervention remained highly cost-effective under all one-way sensitivity analyses.Conclusion:A national hypertension control intervention in India would most likely be budget neutral or cost-saving if the intervention can achieve and maintain high levels of both treatment coverage and adherence.

Timely preterm-birth prediction among pregnant women in Medicaid without preterm-birth history.

To develop and prospectively validate a novel model incorporating claims and community-level socioeconomic data to predict preterm birth at scale among pregnant Medicaid women with no history of preterm birth (PTB). A longitudinal Texas Medicaid cohort study, with 2-year retrospective model building (October 2015-October 2017) and a 1-year prospective model validation phase (January 2018-December 2018). Inclusion criteria were females aged 11 to 55 years with at least 1 live singleton birth and no history of PTB. The primary outcome was live singleton birth earlier than 35 weeks. Covariates were medical/mental/behavioral comorbidities, obstetric history, sociodemographic characteristics, and health services utilization. Of multiple models built, the most parsimonious was selected to classify pregnancies as very high, high, medium, and low risk. Model performance was evaluated using positive predictive value (PPV), sensitivity, case identification ratio (1 / PPV), and timing of prediction. The model was built on 6689 pregnancies and validated on 7855 pregnancies. PTB rate earlier than 35 weeks was approximately 3.3%. Significant risk predictors included prenatal visit attendance, insurance gap days, and medical/obstetrical comorbidities. Model PPV was approximately 4-fold higher for very high-risk women (14.7%) vs cohort (3.3%) and so was the case identification ratio (1:7 vs 1:30, respectively). Sensitivity was good, with 57% of PTBs classified as medium risk or higher. Timing of prediction was clinically relevant, with more than 80% of PTBs risk stratified before 24 weeks. We report a novel PTB prediction model among pregnant Medicaid women without PTB history, which is timely, accurate, practical, and scalable. We leverage Medicaid and community data readily accessible by Medicaid plans to support population-level interventions to prevent PTBs.