primary health care

BACKGROUND
Patients with depression who are treated in primary care practices may receive antidepressants for prolonged periods. Data are limited on the effects of maintaining or discontinuing antidepressant therapy in this setting.

METHODS
We conducted a randomized, double-blind trial involving adults who were being treated in 150 general practices in the United Kingdom. All the patients had a history of at least two depressive episodes or had been taking antidepressants for 2 years or longer and felt well enough to consider stopping antidepressants. Patients who had received citalopram, fluoxetine, sertraline, or mirtazapine were randomly assigned in a 1:1 ratio to maintain their current antidepressant therapy (maintenance group) or to taper and discontinue such therapy with the use of matching placebo (discontinuation group). The primary outcome was the first relapse of depression during the 52-week trial period, as evaluated in a time-to-event analysis. Secondary outcomes were depressive and anxiety symptoms, physical and withdrawal symptoms, quality of life, time to stopping an antidepressant or placebo, and global mood ratings.

RESULTS
A total of 1466 patients underwent screening. Of these patients, 478 were enrolled in the trial (238 in the maintenance group and 240 in the discontinuation group). The average age of the patients was 54 years; 73% were women. Adherence to the trial assignment was 70% in the maintenance group and 52% in the discontinuation group. By 52 weeks, relapse occurred in 92 of 238 patients (39%) in the maintenance group and in 135 of 240 (56%) in the discontinuation group (hazard ratio, 2.06; 95% confidence interval, 1.56 to 2.70; P<0.001). Secondary outcomes were generally in the same direction as the primary outcome. Patients in the discontinuation group had more symptoms of depression, anxiety, and withdrawal than those in the maintenance group.

CONCLUSIONS
Among patients in primary care practices who felt well enough to discontinue antidepressant therapy, those who were assigned to stop their medication had a higher risk of relapse of depression by 52 weeks than those who were assigned to maintain their current therapy. (Funded by the National Institute for Health Research; ANTLER ISRCTN number, ISRCTN15969819.)

IMPORTANCE An estimated 13% of all US adults (18 years or older) have diabetes, and 34.5%
meet criteria for prediabetes. The prevalences of prediabetes and diabetes are higher in older
adults. Estimates of the risk of progression from prediabetes to diabetes vary widely, perhaps
because of differences in the definition of prediabetes or the heterogeneity of prediabetes.
Diabetes is the leading cause of kidney failure and new cases of blindness among adults in the
US. It is also associated with increased risks of cardiovascular disease, nonalcoholic fatty liver
disease, and nonalcoholic steatohepatitis and was estimated to be the seventh leading cause
of death in the US in 2017. Screening asymptomatic adults for prediabetes and type 2
diabetes may allow earlier detection, diagnosis, and treatment, with the ultimate goal of
improving health outcomes.
OBJECTIVE To update its 2015 recommendation, the USPSTF commissioned a systematic
review to evaluate screening for prediabetes and type 2 diabetes in asymptomatic,
nonpregnant adults and preventive interventions for those with prediabetes.
POPULATION Nonpregnant adults aged 35 to 70 years seen in primary care settings who have
overweight or obesity (defined as a body mass index 25 and 30, respectively) and no
symptoms of diabetes.
EVIDENCE ASSESSMENT The USPSTF concludes with moderate certainty that screening for
prediabetes and type 2 diabetes and offering or referring patients with prediabetes to
effective preventive interventions has a moderate net benefit.
CONCLUSIONS AND RECOMMENDATION The USPSTF recommends screening for prediabetes
and type 2 diabetes in adults aged 35 to 70 years who have overweight or obesity. Clinicians
should offer or refer patients with prediabetes to effective preventive interventions.
(B recommendation)

High-quality primary care is the foundation of a high-functioning health care system. When it is high-quality, primary care provides continuous, personcentered, relationship-based care that considers the needs and preferences of individuals, families, and communities. Without access to high-quality primary care, minor health problems can spiral into chronic disease, chronic disease management becomes difficult and uncoordinated, visits to emergency departments increase, preventive care lags, and health care spending soars to unsustainable levels.

Unequal access to primary care remains a concern, and the COVID-19 pandemic amplified pervasive economic, mental health, and social health disparities that ubiquitous, high-quality primary care might have reduced. Primary care is the only health care component where an increased supply is associated with better population health and more equitable outcomes. For this reason, primary care is a common good, which makes the strength and quality of the country’s primary care services a public concern.

The National Academies of Sciences, Engineering, and Medicine formed the Committee on Implementing High-Quality Primary Care in 2019. Building on the recommendations of the 1996 Institute of Medicine report Primary Care: America’s Health in a New Era, the committee was tasked to develop an implementation plan for high-quality primary care in the United States.

The committee’s definition of high-quality primary care (see Box 1) describes what it should be, not what most people in the United States experience today. To rebuild a strong foundation for the U.S. health care system, the committee’s implementation plan includes objectives and actions targeting primary care stakeholders and balancing national needs for scalable solutions while allowing for adaptations to meet local needs.

The committee set five implementation objectives to make high-quality primary care available to all people living in the United States:

1. Pay for primary care teams to care for people, not doctors to deliver services.

2.Ensure that high-quality primary care is available to every individual and family in every community.

3.Train primary care teams where people live and work.

4.Design information technology that serves the patient, family, and the interprofessional care team.

5.Ensure that high-quality primary care is implemented in the United States.

Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing Infrastructure (VINCI) for the years 2006 through 2016. We focused on 4,681,809 veterans who had at least two primary care visits; 829,827 of whom had a hospitalization. Data included 699 million outpatient and 17 million inpatient records. The dependent variable was substance abuse as identified from 89 diagnostic codes using the Agency for Healthcare Quality and Research classification of diseases. In addition, we included the diagnostic codes used for identification of prescription abuse. The independent variables were 10,292 inpatient and 13,512 outpatient diagnoses, plus 71 dummy variables measuring age at different years between 20 and 90 years. A modified naive Bayes model was used to aggregate the risk across predictors. The accuracy of the predictions was examined using area under the receiver operating characteristic (AROC) curve in 20% of data, randomly set aside for the evaluation. Many physical/mental illnesses were associated with substance abuse. These associations supported findings reported in the literature regarding the impact of substance abuse on various diseases and vice versa. In randomly set-aside validation data, the model accurately predicted substance abuse for inpatient (AROC = 0.884), outpatient (AROC = 0.825), and combined inpatient and outpatient (AROC = 0.840) data. If one excludes information available after substance abuse is known, the cross-validated AROC remained high, 0.822 for inpatient and 0.817 for outpatient data. Data within EHRs can be used to detect existing or predict potential future substance abuse.