The positive predictive value of the PHQ-2 as a screener for depression in Spanish-Speaking Latinx, English-speaking Latinx, and non-Latinx White primary care patients.

Abstract BACKGROUND The Canadian Paediatric Society (CPS) recommends that every Canadian physician caring for young children provide an enhanced 18-month well-baby visit including the use of a developmental screening tool, such as the Nipissing District Developmental Screen (NDDS). The Province of Ontario implemented an enhanced 18-month well-baby visit specifically emphasizing the NDDS, which is now widely used in Ontario primary care. However, the diagnostic accuracy of the NDDS in identifying early developmental delays in real-world clinical settings is unknown. OBJECTIVES To assess the predictive validity of the NDDS in primary care for identifying developmental delay and prompting a specialist referral at the 18-month health supervision visit. DESIGN/METHODS This was a prospective longitudinal cohort study enrolling healthy children from primary care practices. Parents completed the 18-month NDDS during their child’s scheduled health supervision visit between January 2012 and February 2015. Using a standardized data collection form, research personnel abstracted data from the child’s health records regarding the child’s developmental outcomes following the 18-month assessment. Data collected included confirmed diagnoses of a development delay, specialist referrals, family history, and interventions. Research personnel were blind to the results of the NDDS. We assessed the diagnostic test properties of the NDDS with a confirmed diagnosis of developmental delay as the criterion measure. The specificity, sensitivity, positive predictive value, and negative predictive value were calculated, with 95% confidence intervals. RESULTS We included 255 children with a mean age of 18.5 months (range, 17.5–20.6) and 139 (55%) were male. 102 (40%) screened positive (1+ flag result on their NDDS). A total of 48 (19%) children were referred, and 23 (9%) had a confirmed diagnosis of a developmental delay (speech and language: 14; gross motor: 4; autism spectrum disorder: 3; global developmental delay: 1; developmental delay: 1). The sensitivity was 74% (95% CI: 52–90%), specificity was 63% (95% CI: 57–70%), positive predictive value was 17% (95% CI:10–25%), and the negative predictive value was 96% (95% CI: 92–99%). CONCLUSION For developmental screening tools, sensitivity between 70%-80% and specificity of 80% have been suggested. The NDDS has moderate sensitivity and specificity in identifying developmental delay at the 18-month health supervision visit. The 1+NDDS flag cut-point may lead to overdiagnosis with more children with typical development being referred, leading to longer wait times for specialist referrals among children in need. Future work includes investigating the diagnostic accuracy of combining the NDDS with other screening tools.

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Three Questions to Screen your Patients for Chemical, Food, and Drug Intolerances the Brief Environmental Exposure and Sensitivity Inventory (BREESI)

10.21203/rs.2.20539/v1 ◽

2020 ◽

Author(s):

Raymond F Palmer ◽

Carlos Roberto Jaén ◽

Roger B. Perales ◽

Rodolfo Rincon ◽

Jacqueline Viramontes ◽

...

Keyword(s):

Primary Care ◽

Positive Predictive Value ◽

Environmental Exposure ◽

Health Care Professionals ◽

Predictive Value ◽

Primary Care Clinic ◽

Diagnostic Tools ◽

Care Clinic ◽

Predictive Values ◽

Chemical Intolerance

Abstract Background: The 50-item Quick Environmental Exposure and Sensitivity Inventory (QEESI) is a validated questionnaire used worldwide to assess intolerances to chemicals, foods, and/or drugs and has become the gold standard for assessing chemical intolerance (CI). Despite a reported prevalence of 8-33%, CI often goes undiagnosed in epidemiological studies and routine primary care. To enhance the QEESI’s utility, we developed the Brief Environmental Exposure and Sensitivity Inventory (BREESI) as a 3-item CI screening instrument. We tested the BREESI’s potential to predict whether an individual is likely to respond adversely to structurally unrelated chemicals, foods, and drugs. Methods: We recruited 286 adult participants from a university-based primary care clinic and through online participation. The positive and negative predictive values of the BREESI items were calculated against the full QEESI scores. Results: 90% of participants answering “yes” to all three items on the BREESI were classified as very suggestive of CI based upon the QEESI chemical intolerance and symptom scores both ≥ 40 (positive predictive value = 90%). For participants endorsing two items, 92% were classified as either very suggestive (39%) or Suggestive (53%) of CI (positive predictive value = 87%). Of those endorsing only one item, only 13% were found to be very suggestive of CI. However, 70% were classified as Suggestive. Of those answering “No” to all of the BREESI items, 99% were classified as not suggestive of CI (i.e., negative predictive value = 99%). Conclusions: The BREESI is a versatile screening tool for rapidly determining potential CI, with clinical and epidemiological applications. Together, the validated BREESI and QEESI provide much needed diagnostic tools that will help inform treatment protocols and teach health care professionals about Toxicant Induced Loss of Tolerance – the mechanism driving CI.

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Positive predictive value and screening performance of GoCheck Kids in a primary care university clinic

Journal of American Association for Pediatric Ophthalmology and Strabismus ◽

10.1016/j.jaapos.2019.11.006 ◽

2020 ◽

Vol 24 (1) ◽

pp. 17.e1-17.e5 ◽

Cited By ~ 2

Author(s):

Megan X. Law ◽

Mariana Flores Pimentel ◽

Catherine E. Oldenburg ◽

Alejandra G. de Alba Campomanes

Keyword(s):

Primary Care ◽

Positive Predictive Value ◽

Predictive Value ◽

Screening Performance

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Adaptation of a Diabetes Self-Management Education and Support Intervention to Inform a Randomized Control Trial in Rural Populations (Preprint)

10.2196/preprints.35664 ◽

2021 ◽

Author(s):

Tamara Oser ◽

Linda Zittleman ◽

Kristen Curcija ◽

Bethany Kwan ◽

Shawnecca Burke ◽

...

Keyword(s):

Primary Care ◽

Rural Communities ◽

Management Education ◽

Advisory Council ◽

Research Network ◽

Rural Populations ◽

Self Management ◽

Community Members ◽

Spanish Speaking ◽

English Speaking

BACKGROUND Over 34 million people in the United States have diabetes, with 1.5 million diagnosed every year. Diabetes self-management education and support (DSMES) is a crucial component of treatment to delay or prevent complications. Rural communities face many unique challenges in accessing DSMES, including geographic barriers and availability of DSMES programs that are culturally adapted to rural context. OBJECTIVE Boot camp translation (BCT) is an established approach to community-based participatory research used to translate complex clinical and scientific information into concepts, messages, and materials that are understandable, meaningful, and relevant to community members and patients. This study aimed to utilize BCT to adapt an existing DSMES program for delivery in rural primary care for English- and Spanish-speaking people with diabetes. METHODS The High Plains Research network (HPRN) Community Advisory Council (C.A.C.) partnered with researchers at the University of Colorado and University of Utah to use BCT to aid in translating medical jargon and materials from an existing DSMES program, called “Diabetes One-Day (D1D).” BCT consisted of 10 virtual meetings over a 6-month period between the C.A.C., which included 15 diverse community stakeholders. Both English-speaking and bilingual Spanish-English speaking CAC members were recruited to reflect the diversity of the rural communities in which the adapted program would be delivered. RESULTS The BCT process guided adaptations to D1D for use in rural settings (R-D1D). R-D1D adaptations reflect both content and delivery to assure that the intervention is appropriate and likely to be accepted by rural English- and Spanish-speaking people with diabetes. Additionally, BCT informed design of recruitment and program materials and identification of recruitment venues. During the BCT process the importance of tailoring materials to reflect culture differences in English- and Spanish-speaking patients was identified. CONCLUSIONS BCT was an effective strategy for academic researchers to partner with rural community members to adapt an existing DSMES intervention for delivery in rural areas to both English- and Spanish-speaking patients with diabetes. Through BCT, adaptations to recruitment materials and methods, program content and delivery, and supplemental materials were developed. The need to culturally adapt Spanish materials with input from stakeholders rather than simply translate materials into Spanish was highlighted. The importance of increasing awareness of the connection between diabetes and depression/diabetes distress, adaptations to include local foods, and the importance of the relationship between people with diabetes and their primary care practices were identified. CLINICALTRIAL Official Title: Adapting and Assessing the Feasibility of a Diabetes Self-management Education and Support Telehealth Intervention for Rural Populations to Reduce Disparities in Diabetes Care ClinicalTrials.gov Identifier: NCT04600622 URL: https://clinicaltrials.gov/ct2/show/NCT04600622?term=oser&cond=diabetes&draw=2&rank=1

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Evaluation of Health Literacy among Spanish-Speaking Primary Care Patients Along the US–Mexico Border

Southern Medical Journal ◽

10.1097/smj.0b013e31825b2468 ◽

2012 ◽

Vol 105 (7) ◽

pp. 334-338 ◽

Cited By ~ 13

Author(s):

Eribeth Penaranda ◽

Marco Diaz ◽

Oscar Noriega ◽

Navkiran Shokar

Keyword(s):

Primary Care ◽

Health Literacy ◽

Spanish Speaking ◽

The Us ◽

Mexico Border ◽

Primary Care Patients ◽

Us Mexico Border

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Satisfaction With Communication in Primary Care for Spanish-Speaking and English-Speaking Parents

Academic Pediatrics ◽

10.1016/j.acap.2017.01.005 ◽

2017 ◽

Vol 17 (4) ◽

pp. 416-423 ◽

Cited By ~ 6

Author(s):

Kori B. Flower ◽

Asheley C. Skinner ◽

H. Shonna Yin ◽

Russell L. Rothman ◽

Lee M. Sanders ◽

...

Keyword(s):

Primary Care ◽

Spanish Speaking ◽

English Speaking

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Structural Validity and Measurement Invariance of the Pearlin Mastery Scale in Spanish-Speaking Primary Care Patients

Evaluation & the Health Professions ◽

10.1177/0163278718774942 ◽

2018 ◽

Vol 41 (3) ◽

pp. 393-399 ◽

Cited By ~ 2

Author(s):

Janna R. Gordon ◽

Vanessa L. Malcarne ◽

Scott C. Roesch ◽

Richard G. Roetzheim ◽

Kristen J. Wells

Keyword(s):

Primary Care ◽

Measurement Invariance ◽

Cultural Backgrounds ◽

Structural Validity ◽

Personal Mastery ◽

Spanish Speaking ◽

Multiple Group ◽

Confirmatory Factor ◽

Primary Care Patients ◽

Multiple Languages

The Pearlin Mastery (PM) Scale is frequently used in health research to assess individuals’ personal mastery or the extent to which they believe they are in control of their own lives. It has been adapted from English into multiple languages including Spanish. However, no studies have assessed the psychometric properties of Spanish translations of the scale. This analysis evaluated structural validity and measurement invariance of the original Spanish translation of the PM Scale in two groups of Spanish-speaking individuals receiving primary care at community clinics in Florida. Confirmatory factor analysis (CFA) indicated that the 5-item version used in the literature yields a unidimensional factor structure as expected; however, multiple-group CFA revealed that the PM Scale items did not load equivalently on the factor across samples. This indicates that the Spanish version of the PM Scale may not measure mastery consistently across groups, possibly due to differences in respondents’ semantic understanding of items or differences in the meaning of the construct itself. Findings suggest that researchers seeking to measure personal mastery in Spanish-speaking participants from diverse cultural backgrounds should consider alternative approaches including the development of new instruments.

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The Utility of the Ces-D as a Depression Screening Measure among Low-Income Women Attending Primary Care Clinics

The International Journal of Psychiatry in Medicine ◽

10.2190/fufr-pk9f-6u10-jxrk ◽

2001 ◽

Vol 31 (1) ◽

pp. 25-40 ◽

Cited By ~ 121

Author(s):

Janet L. Thomas ◽

Glenn N. Jones ◽

Isabel C. Scarinci ◽

Daniel J. Mehan ◽

Phillip J. Brantley

Keyword(s):

Primary Care ◽

Positive Predictive Value ◽

Low Income ◽

Predictive Value ◽

Depression Screening ◽

Care Practice ◽

Primary Care Clinics ◽

Medical Disorders ◽

Cut Score ◽

Low Income Women

Objective: Depressive disorders are among the most common medical disorders seen in primary care practice. The Center for Epidemiologic Studies-Depression (CES-D) scale is one of the measures commonly suggested for detecting depression in these clinics. However, to our knowledge, there have been no previous studies examining the validity of the CES-D among low-income women attending primary care clinics. Method: Low-income women attending public primary care clinics ( n = 179, ages 20–77) completed the CES-D and the Diagnostic Interview Schedule for the DSM-IV (DIS-IV). Results: The results supported the validity of the CES-D. The standard cut-score of 16 and above yielded a sensitivity of .95 and specificity of .70 in predicting Major Depressive Disorder (MDD). However, over two-thirds of those who screened positive did not meet criteria for MDD (positive predictive value = .28). The standard cut-score appears valid, but inefficient for depression screening in this population. An elevated cut-score of 34 yielded a higher specificity (.95) and over 50 percent of the patients who screened positive had a MDD (positive predictive value = .53), but at great cost to sensitivity (.45). Conclusion: Results indicated that the CES-D appears to be as valid for low-income, minority women as for any other demographic group examined in the literature. Despite similar validity, the CES-D appears to be inadequate for routine screening in this population. The positive predictive value remains very low no matter which cut-scores are used. The costs of the false positive rates could be prohibitive, especially in similar public primary care settings.

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Predicting a diagnosis of ankylosing spondylitis using primary care health records – a machine learning approach

10.1101/2021.04.22.21255659 ◽

2021 ◽

Author(s):

Jonathan Kennedy ◽

Natasha Kennedy ◽

Roxanne Cooksey ◽

Ernest Choy ◽

Stefan Siebert ◽

...

Keyword(s):

Machine Learning ◽

Primary Care ◽

Back Pain ◽

Ankylosing Spondylitis ◽

General Population ◽

Positive Predictive Value ◽

Predictive Value ◽

Principal Component ◽

Good Prediction ◽

X Rays

AbstractAnkylosing spondylitis is the second most common cause of inflammatory arthritis. However, a successful diagnosis can take a decade to confirm from symptom onset (via x-rays). The aim of this study was to use machine learning methods to develop a profile of the characteristics of people who are likely to be given a diagnosis of AS in future.The Secure Anonymised Information Linkage databank was used. Patients with ankylosing spondylitis were identified using their routine data and matched with controls who had no record of a diagnosis of ankylosing spondylitis or axial spondyloarthritis. Data was analysed separately for men and women. The model was developed using feature/variable selection and principal component analysis to develop decision trees. The decision tree with the highest average F value was selected and validated with a test dataset.The model for men indicated that lower back pain, uveitis, and NSAID use under age 20 is associated with AS development. The model for women showed an older age of symptom presentation compared to men with back pain and multiple pain relief medications. The models showed good prediction (positive predictive value 70%-80%) in test data but in the general population where prevalence is very low (0.09% of the population in this dataset) the positive predictive value would be very low (0.33%-0.25%).Machine learning can be used to help profile and understand the characteristics of people who will develop AS, and in test datasets with artificially high prevalence, will perform well. However, when applied to a general population with low prevalence rates, such as that in primary care, the positive predictive value for even the best model would be 1.4%. Multiple models may be needed to narrow down the population over time to improve the predictive value and therefore reduce the time to diagnosis of ankylosing spondylitis.

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Developing a Primary Care EMR-based Frailty Definition using Machine Learning

International Journal for Population Data Science ◽

10.23889/ijpds.v3i4.811 ◽

2018 ◽

Vol 3 (4) ◽

Cited By ~ 1

Author(s):

Sylvia Aponte-Hao ◽

Bria Mele ◽

Dave Jackson ◽

Alan Katz ◽

Charles Leduc ◽

...

Keyword(s):

Machine Learning ◽

Primary Care ◽

Positive Predictive Value ◽

Predictive Value ◽

Laboratory Data ◽

Screening Tools ◽

Geriatric Syndrome ◽

Surveillance Network ◽

Clinical Frailty Scale ◽

Definition Of

IntroductionFrailty is a geriatric syndrome that is predictive of heightened vulnerability for disability, hospitalization, and mortality. Annually an estimated 250,000 frail Canadians die, and this estimate is expected to double in the next 40 years, as Canadians grow older. Currently there is no single accepted clinical definition of frailty. Objectives and ApproachThe objective of this study was to develop an operational definition of frailty using machine learning that can be applied to a primary care electronic medical record (EMR) database. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is a pan-Canadian network of primary care practices that collect de-identified patient information (such as encounter diagnoses, health conditions, and laboratory data) from EMRs. 780 patients from CPCSSN have were randomly selected and assessed by physicians using the Rockwood Clinical Frailty Scale (as frail or not frail), and their clinical characteristics from CPCSSN used to develop the definition using machine-learning. ResultsA total of 8,044 clinical features were extracted from these tables: billing, problem list, encounter diagnosis, labs, medications and referrals. A chi-squared automatic interaction detector (CHAID) approach was selected as the best approach. The bootstrapping process used a cost matrix that prioritized high sensitivity and positive predictive value. 10-fold cross validation was used for validity measures. Key features factored into the algorithm included: diagnosis of dementia (ICD-9 code 290), medications furosemide and vitamins, and use of key word “obstruction” within the billing table. The validation measures with 95% confidence intervals are as follows: sensitivity of 28% (95% CI: 21% to 36%), specificity of 94% (95% CI: 93% to 96%), positive predictive value of 53% (95% CI: 42% to 64%), negative predictive value of 86% (95% CI: 83% to 88%). Conclusion/ImplicationsNo other primary care specific frailty screening tools have sufficient validity. These results suggest heterogeneous diseases require clearly defined features and potentially more sophisticated algorithms to account for heterogeneity. Further research utilizing continuous features and continuous frailty scores may be more suitable in the creation of a case detection algorithm.

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