Revisiting the Natural History of Pulmonary Tuberculosis: A Bayesian Estimation of Natural Recovery and Mortality Rates.

BACKGROUND: Tuberculosis (TB) natural history remains poorly characterized, and new investigations are impossible as it would be unethical to follow up TB patients without treatment. METHODS: We considered the reports identified in a previous systematic review of studies from the prechemotherapy era, and extracted detailed data on mortality over time. We used a Bayesian framework to estimate the rates of TB-induced mortality and self-cure. A hierarchical model was employed to allow estimates to vary by cohort. Inference was performed separately for smear-positive TB (SP-TB) and smear-negative TB (SN-TB). RESULTS: We included 41 cohorts of SP-TB patients and 19 cohorts of pulmonary SN-TB patients in the analysis. The median estimates of the TB-specific mortality rates were 0.389 year-1 (95% credible interval [CrI], .335-.449) and 0.025 year-1 (95% CrI, .017-.035) for SP-TB and SN-TB patients, respectively. The estimates for self-recovery rates were 0.231 year-1 (95% CrI, .177-.288) and 0.130 year-1 (95% CrI, .073-.209) for SP-TB and SN-TB patients, respectively. These rates correspond to average durations of untreated TB of 1.57 years (95% CrI, 1.37-1.81) and 5.35 years (95% CrI, 3.42-8.23) for SP-TB and SN-TB, respectively, when assuming a non-TB-related mortality rate of 0.014 year-1 (ie, a 70-year life expectancy). CONCLUSIONS: TB-specific mortality rates are around 15 times higher for SP-TB than for SN-TB patients. This difference was underestimated dramatically in previous TB modeling studies, raising concerns about the accuracy of the associated predictions. Despite being less infectious, SN-TB may be responsible for equivalent numbers of secondary infections as SP-TB due to its much longer duration.

Epidemiology of Trauma History and Body Pain: A Retrospective Study of Community-Based Australian Women.

OBJECTIVE: To assess whether body pain was associated with different trauma histories (physical injury vs. interpersonal injury [IPI]) within Australian women, along with body pain and trauma history associations with biological and psychological (biopsycho) confounders. METHODS: A retrospective cross-sectional analysis was conducted on the Australian Longitudinal Study on Women's Health (ALSWH) 1973-1978 birth cohort wave 6 data. Relevant life events were categorized into two types of traumatic experience and included as exposure variables in a multinomial regression model for body pain subgroups. Also, subgroup analyses considered trauma and pain effects and interactions on biopsycho burden. RESULTS: The unadjusted multinomial regression model revealed that a history of physical injury was found to be significantly associated with body pain severity, as was a history of IPI trauma. After the model was adjusted to include biopsycho confounders, the association between IPI and body pain was no longer significant, and post hoc analysis revealed the relationship was instead mediated by biopsycho confounders. Women with a history of IPI and body pain were also found to have the greatest biopsycho (physical functioning, stress, anxiety, and depression) burden. DISCUSSION: The relationship between IPI and body pain was found to be mediated by biopsycho burden, whereas the relationship between physical injury and body pain was not. Also, a history of IPI was associated with a greater biopsycho burden than was a history of physical injury. These results suggest there is clinical value in considering the comprehensive trauma history of patients with pain when developing their biopsychosocial model of care.

Survival analysis with time-dependent covariates subject to missing data or measurement error: Multiple Imputation for Joint Modeling (MIJM).

Modern epidemiological studies collect data on time-varying individual-specific characteristics, such as body mass index and blood pressure. Incorporation of such time-dependent covariates in time-to-event models is of great interest, but raises some challenges. Of specific concern are measurement error, and the non-synchronous updating of covariates across individuals, due for example to missing data. It is well known that in the presence of either of these issues the last observation carried forward (LOCF) approach traditionally used leads to bias. Joint models of longitudinal and time-to-event outcomes, developed recently, address these complexities by specifying a model for the joint distribution of all processes and are commonly fitted by maximum likelihood or Bayesian approaches. However, the adequate specification of the full joint distribution can be a challenging modeling task, especially with multiple longitudinal markers. In fact, most available software packages are unable to handle more than one marker and offer a restricted choice of survival models. We propose a two-stage approach, Multiple Imputation for Joint Modeling (MIJM), to incorporate multiple time-dependent continuous covariates in the semi-parametric Cox and additive hazard models. Assuming a primary focus on the time-to-event model, the MIJM approach handles the joint distribution of the markers using multiple imputation by chained equations, a computationally convenient procedure that is widely available in mainstream statistical software. We developed an R package "survtd" that allows MIJM and other approaches in this manuscript to be applied easily, with just one call to its main function. A simulation study showed that MIJM performs well across a wide range of scenarios in terms of bias and coverage probability, particularly compared with LOCF, simpler two-stage approaches, and a Bayesian joint model. The Framingham Heart Study is used to illustrate the approach.

Changes in Body Mass Index and Rates of Death and Transplant in Hemodialysis Patients: A Latent Class Joint Modeling Approach.

BACKGROUND: The relationship between body mass index (BMI) and patient survival in end-stage kidney disease is not well understood and has been the subject of much debate over recent years. METHODS: This study used a latent class joint modeling approach to identify latent groups that underpinned associations between patterns of change in BMI during hemodialysis and two competing events: transplant and death without transplant. We included all adult patients who initiated chronic hemodialysis treatment in Australia or New Zealand between 2005 and 2014. RESULTS: There were 16,414 patients included in the analyses; 2,365 (14%) received a transplant, 5,639 (34%) died before transplant, and 8,410 (51%) were administratively censored. Our final model characterized patients based on five broad patterns of weight change (BMI trajectories): "late BMI decline" (about 2 years after commencing hemodialysis); "rapid BMI decline" (immediately after commencing hemodialysis); "stable and normal/overweight BMI"; "stable and morbidly obese BMI"; or "increasing BMI." Mortality rates were highest among classes with declining BMI, and the timing of weight loss coincided with the timing of increases in mortality. Within the two stable BMI classes, death rates were slightly lower among the morbidly obese. CONCLUSIONS: The findings from this descriptive analysis suggest a paradoxical association between obesity and better survival. However, they also suggest that the shape of the BMI trajectory is important, with stable BMI trajectories being beneficial. Future research should be aimed at understanding the causes of weight changes during dialysis, to determine whether there could be strategies to improve patient survival.

The impact of recurrent cytomegalovirus infection on long-term survival in solid organ transplant recipients.

BACKGROUND: Cytomegalovirus (CMV) remains a significant contributor to morbidity and mortality following solid organ transplantation (SOT). While recurrent infection occurs in up to 30% of patients, its impact on mortality is unclear. The aim of this study was to explore the relationship between recurrent CMV infection and long-term survival in SOT recipients. METHODS: We performed a retrospective cohort study of SOT recipients who completed treatment for an episode of CMV infection. Patients were followed until death, loss to follow-up or 10 years following CMV treatment completion. Univariable and multivariable hazard ratios (HR) were calculated, treating relapse and rejection following CMV as time-varying. RESULTS: About 79 kidney, 52 heart, 34 liver, and 5 liver-kidney transplant recipients were included. About 62/170 died, at a median of 3.8 years (IQR 0.8-6.6 years). Median follow-up among the 108 survivors was 7.4 years (IQR 3.7-10 years). Recurrent CMV infection occurred in 49/170 (29%), 67% within 6 months of treatment completion. Mortality among those who relapsed was 39% (19/49) vs 36% (43/121) in those who remained relapse-free (unadjusted HR 1.59, 95% CI 0.92-2.75, P = .10). After adjusting for age and transplanted organ, findings were similar (HR 1.68, 95% CI 0.93-3.04, P = .09). CONCLUSIONS: Mortality following CMV remains high even in the valganciclovir era. Although our findings suggest a possible increased risk of death among patients with recurrent CMV, these did not reach statistical significance. The complex nature of these patients, multiple potential confounders, and limited statistical power made detection of small effects difficult. Larger prospective studies evaluating the clinical impact of strategies to reduce recurrence are needed.

Bayesian Piecewise Linear Mixed Models With a Random Change Point: An Application to BMI Rebound in Childhood.

BACKGROUND: Body mass index (BMI) rebound refers to the beginning of the second rise in BMI during childhood. Accurate estimation of an individual's timing of BMI rebound is important because it is associated with health outcomes in later life. METHODS: We estimated BMI trajectories for 6545 children from the Avon Longitudinal Study of Parents and Children. We used a novel Bayesian two-phase piecewise linear mixed model where the "change point" was an individual-level random effect corresponding to the individual-specific timing of BMI rebound. The model's individual-level random effects (intercept, prechange slope, postchange slope, change point) were multivariate normally distributed with an unstructured variance-covariance matrix, thereby, allowing for correlation between all random effects. RESULTS: Average age at BMI rebound (mean change point) was 6.5 (95% credible interval: 6.4 to 6.6) years. The standard deviation of the individual-specific timing of BMI rebound (random effects) was 2.0 years for females and 1.6 years for males. Correlation between the prechange slope and change point was 0.57, suggesting that faster rates of decline in BMI prior to rebound were associated with rebound occurring at an earlier age. Simulations showed that estimates from the model were less biased than those from models, assuming a common change point for all individuals or a nonlinear trajectory based on fractional polynomials. CONCLUSIONS: Our model flexibly estimated the individual-specific timing of BMI rebound, while retaining parameters that are meaningful and easy to interpret. It is applicable in any situation where one wishes to estimate a change-point process which varies between individuals.

Joint longitudinal hurdle and time-to-event models: an application related to viral load and duration of the first treatment regimen in patients with HIV initiating therapy.

Shared parameter joint models provide a framework under which a longitudinal response and a time to event can be modelled simultaneously. A common assumption in shared parameter joint models has been to assume that the longitudinal response is normally distributed. In this paper, we instead propose a joint model that incorporates a two-part 'hurdle' model for the longitudinal response, motivated in part by longitudinal response data that is subject to a detection limit. The first part of the hurdle model estimates the probability that the longitudinal response is observed above the detection limit, whilst the second part of the hurdle model estimates the mean of the response conditional on having exceeded the detection limit. The time-to-event outcome is modelled using a parametric proportional hazards model, assuming a Weibull baseline hazard. We propose a novel association structure whereby the current hazard of the event is assumed to be associated with the current combined (expected) outcome from the two parts of the hurdle model. We estimate our joint model under a Bayesian framework and provide code for fitting the model using the Bayesian software Stan. We use our model to estimate the association between HIV RNA viral load, which is subject to a lower detection limit, and the hazard of stopping or modifying treatment in patients with HIV initiating antiretroviral therapy. Copyright © 2016 John Wiley & Sons, Ltd.

The Reporting of Treatment Nonadherence and Its Associated Impact on Economic Evaluations Conducted Alongside Randomized Trials: A Systematic Review.

OBJECTIVES: To review trial-based economic evaluations, identifying 1) the proportion reporting adherence, 2) methods for assigning intervention costs according to adherence, 3) which participants were included in the economic analysis, and 4) statistical methods to estimate cost-effectiveness in those who adhered. We provide recommendations on handling nonadherence in economic evaluations. METHODS: The National Health Service Economic Evaluation Database was searched for recently published trials. We extracted information on the methods used to assign shared costs in the presence of nonadherence and methods to account for nonadherence in the economic analysis. RESULTS: Ninety-six eligible trials were identified. For one-off interventions, 86% reported the number of participants initiating treatment. For recurring interventions, 56% and 73%, respectively, reported the number initiating and completing treatment, whereas 66% reported treatment intensity. Most studies (23 of 31 [74%] trials and 42 of 53 [79%] trials of one-off and recurring interventions, respectively) reported strict intention-to-treat or complete case analyses. A minority (3 of 31 [10%] and 7 of 53 [13%], respectively), however, performed a per-protocol analysis. No studies used statistical methods to adjust for nonadherence directly in the economic evaluation. Only 13 studies described patient-level allocation of intervention costs; there was variation in how fixed costs were assigned according to adherence. CONCLUSIONS: Most of the trials reported a measure of adherence, but reporting was not comprehensive. A nontrivial proportion of studies report a primary per-protocol analysis that potentially produces biased results. Alongside primary intention-to-treat analysis, statistical methods for obtaining an unbiased estimate of cost-effectiveness in adherers should be considered.

Comparing measures of multimorbidity to predict outcomes in primary care: a cross sectional study.

BACKGROUND: An increasing proportion of people are living with multiple health conditions, or 'multimorbidity'. Measures of multimorbidity are useful in studies of interventions in primary care to take account of confounding due to differences in case-mix. OBJECTIVES: Assess the predictive validity of commonly used measures of multimorbidity in relation to a health outcome (mortality) and a measure of health service utilization (consultation rate). METHODS: We included 95372 patients registered on 1 April 2005 at 174 English general practices included in the General Practice Research Database. Using regression models we compared the explanatory power of six measures of multimorbidity: count of chronic diseases from the Quality and Outcomes Framework (QOF); Charlson index; count of prescribed drugs; three measures from the John Hopkins ACG software [Expanded Diagnosis Clusters count (EDCs), Adjusted Clinical Groups (ACGs), Resource Utilisation Bands (RUBs)]. RESULTS: A model containing demographics and GP practice alone explained 22% of the uncertainty in consultation rates. The number of prescribed drugs, ACG category, EDC count, RUB category, QOF disease count, or Charlson index increased this to 42%, 37%, 36%, 35%, 30%, and 26%, respectively. Measures of multimorbidity made little difference to the fit of a model predicting 3-year mortality. Nonetheless, Charlson index score was the best performing measure, followed by the number of prescribed drugs. CONCLUSION: The number of prescribed drugs is the most powerful measure for predicting future consultations and the second most powerful measure for predicting mortality. It may have potential as a simple proxy measure of multimorbidity in primary care.

The impact of attrition on the representativeness of cohort studies of older people.

BACKGROUND: There are well-established risk factors, such as lower education, for attrition of study participants. Consequently, the representativeness of the cohort in a longitudinal study may deteriorate over time. Death is a common form of attrition in cohort studies of older people. The aim of this paper is to examine the effects of death and other forms of attrition on risk factor prevalence in the study cohort and the target population over time. METHODS: Differential associations between a risk factor and death and non-death attrition are considered under various hypothetical conditions. Empirical data from the Australian Longitudinal Study on Women's Health (ALSWH) for participants born in 1921-26 are used to identify associations which occur in practice, and national cross-sectional data from Australian Censuses and National Health Surveys are used to illustrate the evolution of bias over approximately ten years. RESULTS: The hypothetical situations illustrate how death and other attrition can theoretically affect changes in bias over time. Between 1996 and 2008, 28.4% of ALSWH participants died, 16.5% withdrew and 10.4% were lost to follow up. There were differential associations with various risk factors, for example, non-English speaking country of birth was associated with non-death attrition but not death whereas being underweight (body mass index < 18.5) was associated with death but not other forms of attrition. Compared to national data, underrepresentation of women with non-English speaking country of birth increased from 3.9% to 7.2% and over-representation of current and ex-smoking increased from 2.6% to 5.8%. CONCLUSIONS: Deaths occur in both the target population and study cohort, while other forms of attrition occur only in the study cohort. Therefore non-death attrition may cause greater bias than death in longitudinal studies. However although more than a quarter of the oldest participants in the ALSWH died in the 12 years following recruitment, differences from the national population changed only slightly.

High-Level Mobility Assessment Tool Normative Values for Children.

BACKGROUND: Physical therapists need to be able to evaluate high-level gross motor skills of children to determine their capacity to engage in activities such as running, jumping, hopping, and stair climbing. The High-Level Mobility Assessment Tool (HiMAT) has excellent interrater and retest reliability and is less susceptible to a ceiling effect than existing mobility scales in children who are 6 to 17 years old and have traumatic brain injury. OBJECTIVE: The purposes of this study were to develop normative HiMAT score ranges for Australian children and to investigate the relationship between children's HiMAT scores and their age, height, weight, and body mass index (BMI). DESIGN: This study used a cross-sectional design. METHODS: Children included in this study were 5 to 12 years old, had no condition affecting their mobility, could follow 2-stage instructions, and had written informed consent from their parent or guardian. A total 1091 children were assessed at their local school, where their height, weight, and HiMAT score were recorded. The relationships between children's age, height, weight, and BMI were summarized using Spearman rank correlations. Truncated regression models were used to determine the most appropriate predictor variable for developing sex-specific normative ranges. RESULTS: There was a positive correlation between children's HiMAT scores and their age, height, weight, and BMI. Age explained the most variability in HiMAT scores for both boys and girls. LIMITATIONS: The reliability, validity, and responsiveness of the HiMAT have not been tested across a broad range of children with mobility limitations. Normative data reported in this study are for Australian children only. CONCLUSIONS: HiMAT scores for children in this study increased with age, height, weight, and BMI. Age was the most appropriate variable for developing a normative dataset of HiMAT scores for children of primary school age.

Joint longitudinal and time-to-event models for multilevel hierarchical data.

Joint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper, we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progression-free survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level higher in the hierarchy than the patient-level, since this has implications for the model formulation.

Predicting long-term survival after coronary artery bypass graft surgery.

OBJECTIVES: To develop a model for predicting long-term survival following coronary artery bypass graft surgery. METHODS: This study included 46 573 patients from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZCTS) registry, who underwent isolated coronary artery bypass graft surgery between 2001 and 2014. Data were randomly split into development (23 282) and validation (23 291) samples. Cox regression models were fitted separately, using the important preoperative variables, for 4 'time intervals' (31-90 days, 91-365 days, 1-3 years and >3 years), with optimal predictors selected using the bootstrap bagging technique. Model performance was assessed both in validation data and in combined data (development and validation samples). Coefficients of all 4 final models were estimated on the combined data adjusting for hospital-level clustering. RESULTS: The Kaplan-Meier mortality rates estimated in the sample were 1.7% at 90 days, 2.8% at 1 year, 4.4% at 2 years and 6.1% at 3 years. Age, peripheral vascular disease, respiratory disease, reduced ejection fraction, renal dysfunction, arrhythmia, diabetes, hypercholesterolaemia, cerebrovascular disease, hypertension, congestive heart failure, steroid use and smoking were included in all 4 models. However, their magnitude of effect varied across the time intervals. Harrell's C-statistics was 0.83, 0.78, 0.75 and 0.74 for 31-90 days, 91-365 days, 1-3 years and >3 years models, respectively. Models showed excellent discrimination and calibration in validation data. CONCLUSIONS: Models were developed for predicting long-term survival at 4 time intervals after isolated coronary artery bypass graft surgery. These models can be used in conjunction with the existing 30-day mortality prediction model.

Increases in waist circumference independent of weight in Mongolia over the last decade: the Mongolian STEPS surveys.

BACKGROUND: In Mongolia, mean waist circumference (WC) has increased dramatically over the last decade, however, it is unknown whether these increases have been greater than corresponding increases in weight. In this study we aimed to assess whether recent increases in WC were greater than expected from changes in weight in Mongolian adults. METHODS: We used data on 13260 Mongolian adults, aged between 18 and 64 years, who participated in one of three (2005, 2009, 2013) nationally representative cross-sectional surveys. Linear regression was used to estimate changes in mean WC over time, adjusted for age, sex, height and weight. We also estimated the age-standardised prevalence for four obesity classification categories (not obese; obese by WC only; obese by body mass index (BMI) only; obese by both BMI and WC) at each survey year. RESULTS: The estimated mean WC in 2009 and 2013, respectively, was 1.26 cm (95% CI: 0.35 to 2.17) and 1.88 cm (95% CI: 1.09 to 2.67) greater compared to 2005, after adjusting for age, sex, height and weight. Between 2005 and 2013, the age-standardised prevalence of those obese according to both BMI and WC increased from 8.0 to 13.6% for men and from 16.5 to 25.5% for women. During the same period, the percentage who were obese by WC only increased from 1.8 to 4.8% for men and from 16.5 to 26.8% for women. In contrast, the percentage who were obese by BMI only remained relatively stable (women: 2.4% in 2005 to 1.0% in 2013; men: 2.7% in 2005 to 4.0% in 2013). CONCLUSION: Over the last decade, among Mongolian adults, there has been substantially greater increase in WC and the prevalence of abdominal obesity than would be expected from increases in weight. Women are at greater risk than men of being misclassified as not obese if obesity is defined using BMI only. Obesity should be monitored using WC in addition to BMI to ensure the prevalence of obesity is not underestimated.

Associations between community-level disaster exposure and individual-level changes in disability and risk of death for older Americans.

Disasters occur frequently in the United States (US) and their impact on acute morbidity, mortality and short-term increased health needs has been well described. However, barring mental health, little is known about the medium or longer-term health impacts of disasters. This study sought to determine if there is an association between community-level disaster exposure and individual-level changes in disability and/or the risk of death for older Americans. Using the US Federal Emergency Management Agency's database of disaster declarations, 602 disasters occurred between August 1998 and December 2010 and were characterized by their presence, intensity, duration and type. Repeated measurements of a disability score (based on activities of daily living) and dates of death were observed between January 2000 and November 2010 for 18,102 American individuals aged 50-89 years, who were participating in the national longitudinal Health and Retirement Study. Longitudinal (disability) and time-to-event (death) data were modelled simultaneously using a 'joint modelling' approach. There was no evidence of an association between community-level disaster exposure and individual-level changes in disability or the risk of death. Our results suggest that future research should focus on individual-level disaster exposures, moderate to severe disaster events, or higher-risk groups of individuals.

Keep it simple? Predicting primary health care costs with clinical morbidity measures.

Models of the determinants of individuals' primary care costs can be used to set capitation payments to providers and to test for horizontal equity. We compare the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs and examine capitation payments based on them. The measures were derived from four morbidity descriptive systems: 17 chronic diseases in the Quality and Outcomes Framework (QOF); 17 chronic diseases in the Charlson scheme; 114 Expanded Diagnosis Clusters (EDCs); and 68 Adjusted Clinical Groups (ACGs). These were applied to patient records of 86,100 individuals in 174 English practices. For a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power. The EDC measures performed best followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Comparisons of predictive power for different morbidity measures were similar for linear and exponential models, but the relative predictive power of the models varied with the morbidity measure. Capitation payments for an individual patient vary considerably with the different morbidity measures included in the cost model. Even for the best fitting model large differences between expected cost and capitation for some types of patient suggest incentives for patient selection. Models with any of the morbidity measures show higher cost for more deprived patients but the positive effect of deprivation on cost was smaller in better fitting models.

Implications of comorbidity for primary care costs in the UK: a retrospective observational study.

BACKGROUND: Comorbidity is increasingly common in primary care. The cost implications for patient care and budgetary management are unclear. AIM: To investigate whether caring for patients with specific disease combinations increases or decreases primary care costs compared with treating separate patients with one condition each. DESIGN: Retrospective observational study using data on 86 100 patients in the General Practice Research Database. METHOD: Annual primary care cost was estimated for each patient including consultations, medication, and investigations. Patients with comorbidity were defined as those with a current diagnosis of more than one chronic condition in the Quality and Outcomes Framework. Multiple regression modelling was used to identify, for three age groups, disease combinations that increase (cost-increasing) or decrease (cost-limiting) cost compared with treating each condition separately. RESULTS: Twenty per cent of patients had at least two chronic conditions. All conditions were found to be both cost-increasing and cost-limiting when co-occurring with other conditions except dementia, which is only cost-limiting. Depression is the most important cost-increasing condition when co-occurring with a range of conditions. Hypertension is cost-limiting, particularly when co-occurring with other cardiovascular conditions. CONCLUSION: Three categories of comorbidity emerge, those that are: cost-increasing, mainly due to a combination of depression with physical comorbidity; cost-limiting because treatment for the conditions overlap; and cost-limiting for no apparent reason but possibly because of inadequate care. These results can contribute to efficient and effective management of chronic conditions in primary care.