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Identifying heavy health care users among primary care patients with chronic non-cancer pain.

ABSTRACT: Objective: The objective of this study was to identify biopsychosocial factors predicting primary care chronic non-cancer pain (CNCP) patients' risk of being heavy health care users. Methods: Patients reporting moderate to severe CNCP for at least 6 months with an active analgesic prescription from a primary care physician were recruited in community pharmacies. Recruited patients completed questionnaires documenting biopsychosocial characteristics. Using administrative databases, direct costs were estimated for health care services used by each patient in the year preceding and following the recruitment. Heavy health care users were defined as patients in the highest annual direct health care costs quartile. Logistic multivariate regression models using the Akaike information criterion were developed to identify predictors of heavy health care use. Results: The median annual direct health care cost incurred by heavy health care users (n = 63) was CAD (Canadian dollars) 7627, versus CAD 1554 for standard health care users (n = 188). The final predictive model of the risks of being a heavy health care user included pain located in the lower body (odds ratio [OR] = 3.03; 95% confidence interval [CI], 1.20-7.65), pain-related disability (OR = 1.24; 95% CI, 1.03-1.48), and health care costs incurred in the year prior to recruitment (OR = 17.67; 95% CI, 7.90-39.48). Variables in the model also included sex, comorbidity, patients' depression level, and attitudes toward medical pain cure. Conclusion: Patients suffering from CNCP in the lower body and showing greater disability were more likely to be heavy health care users, even after adjusting for previous-year direct health care costs. Improving pain management for these patients could have positive impacts on health care use and costs.

SUBMITTER: Antaky E

PROVIDER: S-EPMC8730606 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundChronic pain is a prevalent health concern in the United States (US) and a frequent reason for patients to seek primary care. The challenges associated with developing effective pain management strategies can be perceived as a burden on the patient-provider relationship.ObjectiveThis study explored the relationship between patients' overall satisfaction with their primary care providers (PCPs) and their satisfaction with their chronic pain treatment, as well as the provider behaviors that contributed to chronic pain patients' satisfaction with their PCPs.DesignConcurrent nested mixed-methods design PARTICIPANTS: 97 patients with chronic pain who were assigned to the usual care arm of the Pain Program for Active Coping and Training (PPACT) study.ApproachWe analyzed phone interview and survey data (n?=?97). Interviews assessed provider behaviors that led to patient satisfaction. Interview transcripts were analyzed based on a content analysis approach. Survey responses assessed patient satisfaction with primary care and pain services. We calculated a Pearson's correlation coefficient using five response categories.Key resultsInterviews revealed that high satisfaction with primary care was driven by five concrete PCP behaviors: (1) listening, (2) maintaining communication with patients, (3) acting as an access point to comprehensive pain care, (4) providing an honest assessment of the possibilities of pain care, and (5) taking time during consultations with patients. In surveys, participants reported higher satisfaction with their primary care services than with the pain services they received; these variables were only moderately correlated (r?=?0.586).ConclusionsResults suggest that patients with chronic pain can view the relationship with their PCPs as positive, even in the face of low satisfaction with their pain treatment. The expectations that these patients held of PCPs could be met regardless of providers' ability to successfully relieve chronic pain.

Project description:BackgroundEstimates of chronic pain prevalence using coded primary care data are likely to be substantially lower than estimates derived from community surveys. Most primary care studies have estimated chronic pain prevalence using data searches confined to analgesic medication prescriptions. Increasingly, following recent NICE guideline recommendations, patients and doctors opt for non-drug treatment of chronic pain thus excluding these patients from prevalence estimates based on medication codes. We aimed to develop and test an algorithm combining medication codes with selected diagnostic codes to estimate chronic pain prevalence using coded primary care data.MethodsFollowing a scoping review 4 criteria were developed to identify cohorts of people with chronic pain. These were (1) people with one of 12 ('tier 1') conditions that almost always results in the individual having chronic pain (2) people with one of 20 ('tier 2') conditions included when there are also 3 or more prescription-only analgesics issued in the last 12 months (3) chronic neuropathic pain, or (4) 4 or more prescription-only analgesics issued in the last 12 months. These were translated into 8 logic rules which included 1,932 SNOMED CT codes.ResultsThe algorithm was run on primary care data from 41 GP Practices in Lambeth. The total population consisted of 386,238 GP registered adults ≥ 18 years as of the 31st March 2021. 64,135 (16.6%) were identified as people with chronic pain. This definition demonstrated notably high rates in Black ethnicity females, and higher rates in the most deprived, and older population.ConclusionsEstimates of chronic pain prevalence using structured healthcare data have previously shown lower prevalence estimates for chronic pain than reported in community surveys. This has limited the ability of researchers and clinicians to fully understand and address the complex multifactorial nature of chronic pain. Our study demonstrates that it may be possible to establish more representative prevalence estimates using structured data than previously possible. Use of logic rules offers the potential to move systematic identification and population-based management of chronic pain into mainstream clinical practice at scale and support improved management of symptom burden for people experiencing chronic pain.

Project description:ImportanceOpioid-prescribing policies and guidelines aimed at reducing inappropriate opioid prescribing may lead physicians to stop prescribing opioids. Patients may thus encounter difficulties finding primary care practitioners willing to care for them if they take opioids.ObjectivesTo assess practitioner willingness to accept and continue prescribing opioids to new patients with pain and whether this willingness differs across payer types.Design, setting, and participantsThis survey study used a simulated patient call audit method. A brief telephone survey was administered to all clinics followed by a call using a patient script simulating an adult patient with chronic pain who was taking long-term opioids. The patient had Medicaid or private insurance. Calls were made between June 22 and October 30, 2018, to 667 primary care clinics that served a general adult population in Michigan. Clinics that accepted both Medicaid and private insurance, took new patient appointments, and were successfully recontacted for the simulated call were eligible for the study.Main outcomes and measuresPrevalence of clinics' acceptance of new patients receiving prescription opioids overall and by clinic characteristics and insurance type.ResultsOf the 194 eligible clinics, 94 (48.4%) were randomized according to insurance type to receive calls from research assistants posing as children of patients with Medicaid and 100 (51.5%) to receive calls from those with private insurance. Overall, 79 (40.7%) stated that their practitioners would not prescribe opioids to the simulated patient. Thirty-three clinics (17.0%) requested more information before making a decision. Compared with single-practitioner clinics, clinics with more than 3 practitioners were more likely (odds ratio [OR], 2.99; 95% CI, 1.48-6.04) to accept new patients currently taking opioids. No difference was found in access based on insurance status (OR, 0.92; 95% CI, 0.52-1.64) or whether the clinic offered medications for opioid use disorders (OR, 1.10; 95% CI, 0.45-2.69).Conclusions and relevanceThe findings suggest that access to primary care may be reduced for patients taking prescription opioids, which could lead to unintended consequences, such as conversion to illicit substances or reduced management of other medical comorbidities.

Project description:BackgroundFew studies have categorized high-cost patients (defined by accumulated health care spending above a predetermined percentile) into distinctive groups for which potentially actionable interventions may improve outcomes and reduce costs. We sought to identify homogeneous groups within the persistently high-cost population to develop a taxonomy of subgroups that may be targetable with specific interventions.MethodsWe conducted a retrospective analysis in which we identified adults (≥ 18 yr) who lived in Alberta between April 2014 and March 2019. We defined "persistently high-cost users" as those in the top 1% of health care spending across 4 data sources (the Discharge Abstract Database for inpatient encounters; Practitioner Claims for outpatient primary care and specialist encounters; the Ambulatory Care Classification System for emergency department encounters; and the Pharmaceutical Information Network for medication use) in at least 2 consecutive fiscal years. We used latent class analysis and expert clinical opinion in tandem to separate the persistently high-cost population into subgroups that may be targeted by specific interventions based on their distinctive clinical profiles and the drivers of their health system use and costs.ResultsOf the 3 919 388 adults who lived in Alberta for at least 2 consecutive fiscal years during the study period, 21 115 (0.5%) were persistently high-cost users. We identified 9 subgroups in this population: people with cardiovascular disease (n = 4537; 21.5%); people receiving rehabilitation after surgery or recovering from complications of surgery (n = 3380; 16.0%); people with severe mental health conditions (n = 3060; 14.5%); people with advanced chronic kidney disease (n = 2689; 12.7%); people receiving biologic therapies for autoimmune conditions (n = 2538; 12.0%); people with dementia and awaiting community placement (n = 2520; 11.9%); people with chronic obstructive pulmonary disease or other respiratory conditions (n = 984; 4.7%); people receiving treatment for cancer (n = 832; 3.9%); and people with unstable housing situations or substance use disorders (n = 575; 2.7%).InterpretationUsing latent class analysis supplemented with expert clinical review, we identified 9 policy-relevant subgroups among persistently high-cost health care users. This taxonomy may be used to inform policy, including identifying interventions that are most likely to improve care and reduce cost for each subgroup.

Project description:ObjectivesThe study examined the prevalence, sociodemographic, and clinical correlates of chronic pain among primary care patients in the state of Kerala, India. It also examined the patterns and relationships of chronic physical and mental health conditions with chronic pain.MethodsThis study is a cross-sectional survey conducted among 7165 adult patients selected randomly by a multi-stage stratified design from 71 primary health centers. The questionnaires administered included Chronic pain screening questionnaire, self-reported Chronic physical health condition checklist, Patient Health Questionnaire-SADS, The Alcohol Use Disorders Identification Test, Fagerström Test for Nicotine Dependence, WHO Disability Assessment Schedule and WHOQOL- BREF for Quality/Satisfaction with Life. The prevalence and comorbid patterns of chronic pain were determined. Logistic regression analysis and generalized linear mixed-effects model was employed to examine the relationship of chronic pain to socio-demographic variables and examined physical and mental health conditions.ResultsA total of 1831 (27%) patients reported chronic pain. Among those with chronic pain, 28.3% reported no co-occurring chronic mental or physical illness, 35.3% reported one, and 36.3% reported multi-morbidity. In the multivariate analysis, patients with chronic pain when compared to those without had higher odds of being older, female, having lower education, not living with their family, greater disability, and poor satisfaction with life. Chronic pain was independently associated with both medical (hypertension, diabetes mellitus, tuberculosis, arthritis, and other medical illnesses) and mental health conditions (depressive disorders, anxiety disorders, and tobacco dependence). It showed a varying strength of association and additive effect with increasing number of co-occurring physical and mental illnesses.ConclusionsChronic pain is a common condition among primary care attendees associated with significant burden of medical and mental health comorbidity. The findings highlight the need to incorporate treatment models that will ensure appropriate management to improve outcomes within the resource constraints.

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Identifying heavy health care users among primary care patients with chronic non-cancer pain.

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