Patterns of primary care and mortality among patients with schizophrenia or diabetes: a cluster analysis approach to the retrospective study of healthcare utilization

Abstract Background Patients with schizophrenia have difficulty managing their medical healthcare needs, possibly resulting in delayed treatment and poor outcomes. We analyzed whether patients reduced primary care use over time, differentially by diagnosis with schizophrenia, diabetes, or both schizophrenia and diabetes. We also assessed whether such patterns of primary care use were a significant predictor of mortality over a 4-year period. Methods The Veterans Healthcare Administration (VA) is the largest integrated healthcare system in the United States. Administrative extracts of the VA's all-electronic medical records were studied. Patients over age 50 and diagnosed with schizophrenia in 2002 were age-matched 1:4 to diabetes patients. All patients were followed through 2005. Cluster analysis explored trajectories of primary care use. Proportional hazards regression modelled the impact of these primary care utilization trajectories on survival, controlling for demographic and clinical covariates. Results Patients comprised three diagnostic groups: diabetes only (n = 188,332), schizophrenia only (n = 40,109), and schizophrenia with diabetes (Scz-DM, n = 13,025). Cluster analysis revealed four distinct trajectories of primary care use: consistent over time, increasing over time, high and decreasing, low and decreasing. Patients with schizophrenia only were likely to have low-decreasing use (73% schizophrenia-only vs 54% Scz-DM vs 52% diabetes). Increasing use was least common among schizophrenia patients (4% vs 8% Scz-DM vs 7% diabetes) and was associated with improved survival. Low-decreasing primary care, compared to consistent use, was associated with shorter survival controlling for demographics and case-mix. The observational study was limited by reliance on administrative data. Conclusion Regular primary care and high levels of primary care were associated with better survival for patients with chronic illness, whether psychiatric or medical. For schizophrenia patients, with or without comorbid diabetes, primary care offers a survival benefit, suggesting that innovations in treatment retention targeting at-risk groups can offer significant promise of improving outcomes.http://deepblue.lib.umich.edu/bitstream/2027.42/78274/1/1472-6963-9-127.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78274/2/1472-6963-9-127.pdfPeer Reviewe

Reciprocal learning and chronic care model implementation in primary care: results from a new scale of learning in primary care

<p>Abstract</p> <p>Background</p> <p>Efforts to improve the care of patients with chronic disease in primary care settings have been mixed. Application of a complex adaptive systems framework suggests that this may be because implementation efforts often focus on education or decision support of individual providers, and not on the dynamic system as a whole. We believe that learning among clinic group members is a particularly important attribute of a primary care clinic that has not yet been well-studied in the health care literature, but may be related to the ability of primary care practices to improve the care they deliver.</p> <p>To better understand learning in primary care settings by developing a scale of learning in primary care clinics based on the literature related to learning across disciplines, and to examine the association between scale responses and chronic care model implementation as measured by the Assessment of Chronic Illness Care (ACIC) scale.</p> <p>Methods</p> <p>Development of a scale of learning in primary care setting and administration of the learning and ACIC scales to primary care clinic members as part of the baseline assessment in the ABC Intervention Study. All clinic clinicians and staff in forty small primary care clinics in South Texas participated in the survey.</p> <p>Results</p> <p>We developed a twenty-two item learning scale, and identified a five-item subscale measuring the construct of reciprocal learning (Cronbach alpha 0.79). Reciprocal learning was significantly associated with ACIC total and sub-scale scores, even after adjustment for clustering effects.</p> <p>Conclusions</p> <p>Reciprocal learning appears to be an important attribute of learning in primary care clinics, and its presence relates to the degree of chronic care model implementation. Interventions to improve reciprocal learning among clinic members may lead to improved care of patients with chronic disease and may be relevant to improving overall clinic performance.</p

Differential Effects of Comorbidity on Antihypertensive and Glucose-Regulating Treatment in Diabetes Mellitus – A Cohort Study

BACKGROUND: Comorbidity is often mentioned as interfering with "optimal" treatment decisions in diabetes care. It is suggested that diabetes- related comorbidity will increase adequate treatment, whereas diabetes- unrelated comorbidity may decrease this process of care. We hypothesized that these effects differ according to expected priority of the conditions. METHODS: We evaluated the relationship between comorbidity and treatment intensification in a study of 11,248 type 2 diabetes patients using the GIANTT (Groningen Initiative to Analyse type 2 diabetes Treatment) database. We formed a cohort of patients with a systolic blood pressure >/= 140 mmHg (6,820 hypertensive diabetics), and a cohort of patients with an HbA1c >/= 7% (3,589 hyperglycemic diabetics) in 2007. We differentiated comorbidity by diabetes-related or unrelated conditions and by priority. High priority conditions include conditions that are life- interfering, incident or requiring new medication treatment. We performed Cox regression analyses to assess association with treatment intensification, defined as dose increase, start, or addition of drugs. RESULTS: In both the hypertensive and hyperglycemic cohort, only patients with incident diabetes-related comorbidity had a higher chance of treatment intensification (HR 4.48, 2.33-8.62 (p<0.001) for hypertensives; HR 2.37, 1.09-5.17 (p = 0.030) for hyperglycemics). Intensification of hypertension treatment was less likely when a new glucose-regulating drug was prescribed (HR 0.24, 0.06-0.97 (p = 0.046)). None of the prevalent or unrelated comorbidity was significantly associated with treatment intensification. CONCLUSIONS: Diabetes-related comorbidity induced better risk factor treatment only for incident cases, implying that appropriate care is provided more often when complications occur. Diabetes- unrelated comorbidity did not affect hypertension or hyperglycemia management, even when it was incident or life-interfering. Thus, the observed "undertreatment" in diabetes care cannot be explained by constraints caused by such comorbidity

The devil is in the details: trends in avoidable hospitalization rates by geography in British Columbia, 1990–2000

BACKGROUND: Researchers and policy makers have focussed on the development of indicators to help monitor the success of regionalization, primary care reform and other health sector restructuring initiatives. Certain indicators are useful in examining issues of equity in service provision, especially among older populations, regardless of where they live. AHRs are used as an indicator of primary care system efficiency and thus reveal information about access to general practitioners. The purpose of this paper is to examine trends in avoidable hospitalization rates (AHRs) during a period of time characterized by several waves of health sector restructuring and regionalization in British Columbia. AHRs are examined in relation to non-avoidable and total hospitalization rates as well as by urban and rural geography across the province. METHODS: Analyses draw on linked administrative health data from the province of British Columbia for 1990 through 2000 for the population aged 50 and over. Joinpoint regression analyses and t-tests are used to detect and describe trends in the data. RESULTS: Generally speaking, non-avoidable hospitalizations constitute the vast majority of hospitalizations in a given year (i.e. around 95%) with AHRs constituting the remaining 5% of hospitalizations. Comparing rural areas and urban areas reveals that standardized rates of avoidable, non-avoidable and total hospitalizations are consistently higher in rural areas. Joinpoint regression results show significantly decreasing trends overall; lines are parallel in the case of avoidable hospitalizations, and lines are diverging for non-avoidable and total hospitalizations, with the gap between rural and urban areas being wider at the end of the time interval than at the beginning. CONCLUSION: These data suggest that access to effective primary care in rural communities remains problematic in BC given that rural areas did not make any gains in AHRs relative to urban areas under recent health sector restructuring initiatives. It remains important to continue to monitor the discrepancy between them as a reflection of inequity in service provision. In addition, it is important to consider alternative explanations for the observed trends paying particular attention to the needs of rural and urban populations and the factors influencing local service provision

I Believe, Therefore I Do

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A pilot survey of post-deployment health care needs in small community-based primary care clinics

<p>Abstract</p> <p>Background</p> <p>Relatively little is known regarding to what extent community-based primary care physicians are encountering post-deployment health care needs among veterans of the Afghanistan or Iraq conflicts and their family members.</p> <p>Methods</p> <p>This pilot study conducted a cross-sectional survey of 37 primary care physicians working at small urban and suburban clinics belonging to a practice-based research network in the south central region of Texas.</p> <p>Results</p> <p>Approximately 80% of the responding physicians reported caring for patients who have been deployed to the Afghanistan or Iraq war zones, or had a family member deployed. Although these physicians noted a variety of conditions related to physical trauma, mental illnesses and psychosocial disruptions such as marital, family, financial, and legal problems appeared to be even more prevalent among their previously deployed patients and were also noted among family members of deployed veterans.</p> <p>Conclusions</p> <p>Community-based primary care physicians should be aware of common post-deployment health conditions and the resources that are available to meet these needs.</p

ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence

Background: The possibilities offered by next generation sequencing (NGS) platforms are revolutionizing biotechnological laboratories. Moreover, the combination of NGS sequencing and affordable high-throughput genotyping technologies is facilitating the rapid discovery and use of SNPs in non-model species. However, this abundance of sequences and polymorphisms creates new software needs. To fulfill these needs, we have developed a powerful, yet easy-to-use application. Results: The ngs_backbone software is a parallel pipeline capable of analyzing Sanger, 454, Illumina and SOLiD (Sequencing by Oligonucleotide Ligation and Detection) sequence reads. Its main supported analyses are: read cleaning, transcriptome assembly and annotation, read mapping and single nucleotide polymorphism (SNP) calling and selection. In order to build a truly useful tool, the software development was paired with a laboratory experiment. All public tomato Sanger EST reads plus 14.2 million Illumina reads were employed to test the tool and predict polymorphism in tomato. The cleaned reads were mapped to the SGN tomato transcriptome obtaining a coverage of 4.2 for Sanger and 8.5 for Illumina. 23,360 single nucleotide variations (SNVs) were predicted. A total of 76 SNVs were experimentally validated, and 85% were found to be real. Conclusions: ngs_backbone is a new software package capable of analyzing sequences produced by NGS technologies and predicting SNVs with great accuracy. In our tomato example, we created a highly polymorphic collection of SNVs that will be a useful resource for tomato researchers and breeders. The software developed along with its documentation is freely available under the AGPL license and can be downloaded from http://bioinf. comav.upv.es/ngs_backbone/ or http://github.com/JoseBlanca/franklin.Blanca Postigo, JM.; Pascual Bañuls, L.; Ziarsolo Areitioaurtena, P.; Nuez Viñals, F.; Cañizares Sales, J. (2011). Ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence. BMC Genomics. 12:1-8. doi:10.1186/1471-2164-12-285S1812Metzker ML: Sequencing technologies - the next generation. Nature Reviews Genetics. 2010, 11 (1): 31-46. 10.1038/nrg2626.454 sequencing. [ http://www.454.com/ ]Illumina Inc. [ http://www.illumina.com/ ]Flicek P, Birney E: Sense from sequence reads: methods for alignment and assembly (vol 6, pg S6, 2009). 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[ http://1000genomes.org/wiki/doku.php?id=1000_genomes:analysis:vcf4.0 ]The seqanswers internet forum. [ http://seqanswers.com/ ]Blankenberg D, Taylor J, Schenck I, He JB, Zhang Y, Ghent M, Veeraraghavan N, Albert I, Miller W, Makova KD, Ross CH, Nekrutenko A: A framework for collaborative analysis of ENCODE data: Making large-scale analyses biologist-friendly. Genome Research. 2007, 17 (6): 960-964. 10.1101/gr.5578007.CloVR Automated Sequence Analysis from Your Desktop. [ http://clovr.org/ ]Papanicolaou A, Stierli R, Ffrench-Constant RH, Heckel DG: Next generation transcriptomes for next generation genomes using est2assembly. Bmc Bioinformatics. 2009, 10:Applied Biosystems by life technologies. [ http://www.appliedbiosystems.com/absite/us/en/home/applications-technologies/solid-next-generation-sequencing.html ]Wall PK, Leebens-Mack J, Chanderbali AS, Barakat A, Wolcott E, Liang HY, Landherr L, Tomsho LP, Hu Y, Carlson JE, Ma H, Schuster SC, Soltis DE, Soltis PS, Altman N, dePamphilis CW: Comparison of next generation sequencing technologies for transcriptome characterization. Bmc Genomics. 2009, 10:Murchison EP, Tovar C, Hsu A, Bender HS, Kheradpour P, Rebbeck CA, Obendorf D, Conlan C, Bahlo M, Blizzard CA, Pyecroft S, Kreiss A, Kellis M, Stark A, Harkins TT, Marshall Graves JA, Woods GM, Hanon GJ, Papenfuss AT: The Tasmanian Devil Transcriptome Reveals Schwann Cell Origins of a Clonally Transmissible Cancer. Science. 2010, 327 (5961): 84-87. 10.1126/science.1180616.Parchman TL, Geist KS, Grahnen JA, Benkman CW, Buerkle CA: Transcriptome sequencing in an ecologically important tree species: assembly, annotation, and marker discovery. 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Reasons of general practitioners for not prescribing lipid-lowering medication to patients with diabetes: a qualitative study

Background: Lipid-lowering medication remains underused, even in high-risk populations. The objective of this study was to determine factors underlying general practitioners' decisions not to prescribe such drugs to patients with type 2 diabetes. Methods: A qualitative study with semi-structured interviews using real cases was conducted to explore reasons for not prescribing lipid-lowering medication after a guideline was distributed that recommended the use of statins in most patients with type 2 diabetes. Seven interviews were conducted with general practitioners (GPs) in The Netherlands, and analysed using an analytic inductive approach. Results: Reasons for not-prescribing could be divided into patient and physician-attributed factors. According to the GPs, some patients do not follow-up on agreed medication and others object to taking lipid-lowering medication, partly for legitimate reasons such as expected or perceived side effects. Furthermore, the GPs themselves perceived reservations for prescribing lipid-lowering medication in patients with short life expectancy, expected compliance problems or near goal lipid levels. GPs sometimes postponed the start of treatment because of other priorities. Finally, barriers were seen in the GPs' practice organisation, and at the primary-secondary care interface. Conclusion: Some of the barriers mentioned by GPs seem to be valid reasons, showing that guideline non-adherence can be quite rational. On the other hand, treatment quality could improve by addressing issues, such as lack of knowledge or motivation of both the patient and the GP. More structured management in general practice may also lead to better treatment

The breadth of primary care: a systematic literature review of its core dimensions

Background: Even though there is general agreement that primary care is the linchpin of effective health care delivery, to date no efforts have been made to systematically review the scientific evidence supporting this supposition. The aim of this study was to examine the breadth of primary care by identifying its core dimensions and to assess the evidence for their interrelations and their relevance to outcomes at (primary) health system level. Methods: A systematic review of the primary care literature was carried out, restricted to English language journals reporting original research or systematic reviews. Studies published between 2003 and July 2008 were searched in MEDLINE, Embase, Cochrane Library, CINAHL, King's Fund Database, IDEAS Database, and EconLit. Results: Eighty-five studies were identified. This review was able to provide insight in the complexity of primary care as a multidimensional system, by identifying ten core dimensions that constitute a primary care system. The structure of a primary care system consists of three dimensions: 1. governance; 2. economic conditions; and 3. workforce development. The primary care process is determined by four dimensions: 4. access; 5. continuity of care; 6. coordination of care; and 7. comprehensiveness of care. The outcome of a primary care system includes three dimensions: 8. quality of care; 9. efficiency care; and 10. equity in health. There is a considerable evidence base showing that primary care contributes through its dimensions to overall health system performance and health. Conclusions: A primary care system can be defined and approached as a multidimensional system contributing to overall health system performance and health