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Research ArticleOriginal Research
Impact of Social Determinants of Health on Patients with Complex Diabetes Who Are Served by National Safety-Net Health Centers
Vivian Li, Mary Ann McBurnie, Melissa Simon, Phil Crawford, Michael Leo, Fred Rachman, Erika Cottrell, Lydia Dant, Mary Oneha and Rosy Chang Weir
The Journal of the American Board of Family Medicine May 2016, 29 (3) 356-370; DOI: https://doi.org/10.3122/jabfm.2016.03.150226
Vivian Li
the Association of Asian Pacific Community Health Organizations, San Leandro CA (VL, RCW); Kaiser Permanente Center for Health Research, Portland OR (MAB, PC, ML); Northwestern University, Feinberg School of Medicine, Chicago IL (MS); Alliance of Chicago Community Health Services, Chicago IL (FR); OCHIN, Inc., Portland OR (EC); The Fenway Institute, Boston, MA (LD); and the Waimanalo Health Center, Waimanalo, HI (MO).
Mary Ann McBurnie
Melissa Simon
Phil Crawford
Michael Leo
Fred Rachman
Erika Cottrell
Lydia Dant
Mary Oneha
Rosy Chang Weir
Objective: Recent research demonstrates an increased need to understand the contribution of social determinants of health (SDHs) in shaping an individual's health status and outcomes. We studied patients with diabetes in safety-net centers and evaluated associations of their disease complexity, demographic characteristics, comorbidities, insurance status, and primary language with their HbA1c level over time.
Methods: Adult patients with diabetes with at least 3 distinct primary care visits between January 1, 2006, and December 31, 2013, were identified in the CHARN data warehouse. These patients were categorized into 4 groups: those without a diagnosis of cardiovascular disease (CVD) or depression; those with CVD but not depression; those with depression but not CVD; and those with CVD and depression. Charlson score; demographic characteristics such as age, sex, and race/ethnicity; and SDHs such as primary language and insurance status were used as predictors. The outcome measure was HbA1c. Hypothesis testing was conducted using 3-level hierarchical linear models.
Results: Baseline HbA1c differed significantly across the 4 diabetes groups and by race/ethnicity. The amount of HbA1c change over time differed by insurance status. Patients who were continuously insured tended to have lower baseline HbA1c and a smaller increase. Chinese-speaking patients tended to have lower baseline HbA1c but a larger increase over time compared with English speakers. There were various unexpected associations: compared with the diabetes-only group, mean HbA1c tended to be lower among the other more complex groups at baseline; women tended to have lower measures at baseline; older age and higher Charlson scores were associated with lower HbA1c.
Conclusions: There is still unexplained variability relating to both baseline HbA1c values and change over time in the model. SDHs, such as insurance status and primary language, are associated with HbA1c, and results suggest that these relationships vary with disease status among patients with diabetes in safety-net centers. It is important to recognize that there are complex relationships among demographic and SDH measures in complex patients, and there is work to be done in correctly modeling and understanding these relationships. We also recommend prioritizing the collection of SDH and enabling services data for safety-net patients that would be instrumental in conducting a more comprehensive study.
Safety-Net Providers
Social determinants of health (SDHs) contribute to disparities in health and a higher prevalence of health-related problems, particularly for medically underserved populations. The World Health Organization Commission on Social Determinants of Health defines SDHs as "the structural determinants and conditions of daily life responsible for a major part of health inequities between and within countries."1 SDHs play a powerful role in shaping health across a wide range of health indicators, settings, and populations.1⇓⇓⇓–5 Traditional medical care is not the only influence on health, and the effects of medical care may be more limited than commonly thought, particularly in determining who becomes sick or injured.3⇓⇓⇓–7 Interest in studying and describing SDHs has increased over the past few decades,8⇓–10 and it is clear that SDHs are associated with suboptimal health status or poor health-related outcomes,11 such as higher rates of mental disorders and medical conditions.12 In a meta-analysis conducted by Lorant and colleagues,13 people with a low socioeconomic status were 1.8 times more likely to report being depressed than were people with higher status. Lett et al14 also found that low social support was associated with a 1.5- to 2-times increased the risk of developing coronary heart disease or experiencing adverse outcomes associated with coronary heart disease.
Community health centers (CHCs) are the health and medical homes for over 22 million people in the United States, primarily populations that have a disproportionately low income (93%), are uninsured (35%), or have limited English proficiency (23%).15 Studying these populations provides a unique opportunity to understand and characterize the effect of SDHs on health care use and health outcomes, and to better identify the factors that increase the risk of poor health. Typically, CHC patients are poorer, more racially and ethnically diverse, more likely to report being in fair or poor health, and more likely to be unemployed and uninsured than the broader low-income population.16 For example, the rate of having diabetes mellitus (DM) or borderline DM is 19.2% among the CHC population versus 12.0% among the general low-income US population; the rate of having ≥2 health conditions (DM, asthma, hypertension, a liver condition, coronary heart disease, and emphysema) in a lifetime is 25.4% for CHC patients but 16.6% for the general low-income US population; and the rate of being uninsured is 34.4% for CHC patients versus 12.6% for the general low-income US population.16 Moreover, a large proportion of patients who receive care at CHCs have complex health needs, including multiple chronic diseases and behavioral health conditions (eg, depression or substance abuse).17 Among the broader US population, those with behavioral health disorders have significantly higher rates of having comorbid medical conditions,12 a higher incidence of DM,12 worse DM outcomes over time,18 and a higher risk of death from DM-related complications.19 Moreover, the use of nonpreventive medical services (eg, in an emergency department, hospitalization, 30-day readmission, avoidable hospitalization) is significantly higher among patients with behavioral health conditions than those without.20,21 Thus it is important to understand the relationship between behavioral health, medical comorbidities, and SDHs in CHC settings to understand how best to serve these high-risk populations.
The Community Health Applied Research Network (CHARN), which was established in 2010, provides an opportunity to examine some of the relationships between behavioral health, medical comorbidities, and SDHs. CHARN consists of 4 safety-net research nodes, each of which has affiliated CHCs and an academic or research partner. Each node includes ≥3 CHCs (17 total). The participating CHCs are located in 9 states across the country, and all have provided standardized data to the centralized CHARN Data Warehouse (CDW). The 4 nodes are Alliance of Chicago Community Health Services (Alliance; Chicago, IL); the Association of Asian Pacific Community Health Organizations (AAPCHO; San Leandro, CA); Fenway Health (Fenway; Boston, MA); and OCHIN (formerly the Oregon Community Health Information Network but shortened to OCHIN when other states joined; Portland, OR).22 Figure 1 shows the structure of CHARN.
Structure of the Community Health Applied Research Network (CHARN).22 *Overall governance of the network is the responsibility of the CHARN Steering Committee (SC). The SC is responsible for approving final decisions, establishing subcommittees, affirming their charters, and appointing subcommittee chairs. †Asian Health Services, CA; Charles B. Wang Community Health Center, NY; Waianae Coast Comprehensive Health Center, HI; Waimanalo Health Center, HI. ‡Erie Family Health Center, IL; Glide Health Services, CA; Heartland Health Outreach, IL; Howard Brown Health Center, IL; Near North, IL; North Country Health Care, AZ; PCC Community Wellness Center, IL. §Beaufort Jasper Hampton Comprehensive Health Services, SC; Chase Brexton Health Services, MD; Fenway Health, MA. ¶Multnomah County Health Department, OR; Richmond Clinic, OR; Open Door Community Health Center, CA; Virginia Garcia Memorial Health Center, OR. AAPCHO, Association of Asian Pacific Community Health Organizations; DCC, Data Coordinating Center at the Kaiser Permanente Center for Health Research, Portland, Oregon; UCLA, University of California, Los Angeles.
For this retrospective study, we identified diabetic patients in the CDW and determined whether they also had a diagnosis of cardiovascular disease (CVD), depression, or both. DM was chosen as the focus because of the potential complexity of the disease and its treatment (ie, with or without comorbidities). In addition, DM is well documented in electronic health records (EHRs), as is its outcome measure, glycohemoglobin (HbA1c) level, which enabled us to use EHR data across CHARN CHCs from the centralized CDW. Because 19% of health center patients across the nation have DM or prediabetes,16 it is also important to learn more about these patients to better allocate limited CHC resources to improve health outcomes. CVD also poses a significant health burden in vulnerable populations, and depression is a common mental condition among CHC patients that is closely related to outcomes of other medical conditions.12 In this article we evaluate the association between DM disease status and change in HbA1c level over time, and we test whether adverse HbA1c trajectories are associated with SDH factors. We treated 2 SDHs—primary language and insurance continuity—as potential barriers to health care for CHC patients.23⇓⇓⇓–27 Language barriers in the health care setting can lead to a delay or denial of services, issues with medication management, and underuse of preventive services.23⇓–25 Compared with insured patients, patients without insurance tend to have worse access to care, less continuity of care, more unmet needs, and worse outcomes.26,27 The CDW includes patient-level demographic information, insurance coverage status, primary language, and disease status. We hypothesized that diabetic patients with more complex health profiles (ie, patients with comorbidities) experience more adverse HbA1c trajectories (change over time) than less complex patients and that these trajectories are associated with demographic and SDH characteristics.
We retrospectively identified nonpregnant patients in the CDW who had at least 1 primary care visit per year for 3 consecutive years and who were aged ≥18 years on December 31, 2009. Of these, patients with at least 2 visits with DM-related International Classification of Diseases, 9th Revision (ICD-9) codes were considered to have DM28; the DM index date was defined as the date of the second visit at which DM was identified.
We used version 2 (November 2014) of the CDW, which contains EHR data on nearly 1 million patients who made ≥1 primary care visits to any of CHARN′s 17 CHCs between January 1, 2006, and December 31, 2013. The CDW is housed within the CHARN data coordinating center and is stored behind a secure firewall on an SQL Server (R2012) using industry-standard encryption at the Kaiser Permanente Center for Health Research. The research nodes upload the EHR-based CHC limited data sets to the secure website at the data coordinating center from their local data warehouses, which conform to a standardized SQL relational database schema.29 All CHARN studies, including this one, have received institutional review board approval.
All information was retrospectively collected from the CDW. Self-reported measures included age calculated as of 2013, sex, race/ethnicity, and primary language. Insurance status was determined based on documented coverage at each encounter and was coded according to whether patients had been continuously insured across all encounters, had gaps in insurance coverage (ie, a lapse of insurance coverage of >30 days), or had been continuously uninsured. To assess health care use, we calculated the average annual number of visits. We evaluated primary language and insurance status as our SDH factors.
To adjust our analyses for patients' overall comorbidity burden, we computed the Charlson index score based on the ICD-9 codes documented in the EHR during a 12-month period (between 10 months before and 2 months after the index date). The Charlson index was designed to estimate the long-term prognosis and lethality of comorbid patients and is based on a point-scoring system (from 0 to 40) for the presence of specific associated diseases. Points are accumulated according to the associated diseases and a single point for each 10 years of age for patients older than 40 (in 50 years, 1 point; 60 years, 2 points; etc.). The distinguishing feature and undisputed advantage of the Charlson index is its ability to evaluate a patient's age and determine his or her mortality rate, which in the absence of comorbidity (0 points) is 12%, at 1 or 2 points is 26%, at 3 or 4 points is 52%, and at ≥5 points is 85%. In 1992 Deyo et al30 added to this index chronic forms of ischemic cardiac disorder and the stages of chronic cardiac insufficiency.
Diabetic patients were categorized using ICD-9 codes as: without a diagnosis of CVD or depression (DM-only); with only a diagnosis of CVD (DM + CVD); with only a diagnosis of depression (DM + DEP); or with diagnoses of both CVD and depression (DM + CVD + DEP). CVD or depression diagnosed within 60 days of the DM index date was considered a comorbidity. The outcome measure in this study was change in HbA1c level over time. ICD-9 codes used to define these conditions are listed in Table 1.
International Classification of Diseases, 9th Revision Codes of Diabetes, Cardiovascular Disease, and Depression
After careful discussion, we chose the above-mentioned variables to describe the patients' basic demographics, SDHs, and disease status without having to include so many variables that the models would not have enough power. We considered other variables such as homeless status and federal poverty level, but because of the incompleteness of the data in CDW version 2.2, we were not able to include them in the study.
Population characteristics are described using means and standard deviations (SDs), percentages, and counts. Significance tests for differences in patient characteristics between disease groups were not performed because we did not have hypotheses of interest related to these comparisons. Observed differences are described for selected characteristics to orient the reader to the population studied. The hypothesis was tested using 3-level hierarchical linear models in a growth curve framework31⇓–33 to account for correlated observations of repeated measures within patients and for patients within CHCs. The first level of the model included time (years from index diagnosis) as a predictor, thus modeling the within-person variation. The second level included dummy variables for the DM groupings (referent: DM-only group) and for the other categorical (also dummy coded) and continuous person-level predictors for both the intercept and slope for time random effects. Person-level predictors included age, Charlson index score, sex, race/ethnicity, insurance status, primary language, and average number of annual primary care visits. The third level of the model was the CHC level and included node (dummy coded) as a predictor of the level-2 intercept and slope random effects. The interpretation of a significant coefficient for a given nonreferent group (ie, DM + CVD, DM + DEP, or DM + CVD + DEP) on the intercept indicates that the nonreferent group differs from the referent group (DM only) on HbA1c level at the time of the DM diagnosis, whereas a significant coefficient for the slope of time indicates that there are different trajectories across time. These interpretations assume that all other variables are held constant. We assessed interactions with DM status for the SDH measures insurance status and primary language.
We modeled all available HbA1c values collected from patients from their index date through 2013. Models estimated index and slope coefficients (β) corresponding to node and to each of the person-level measures described above. Because of the complexity of this base model, we used separate models to examine the interaction effects between DM status and each individual covariate. We probed significant interactions by graphing the simple-effects equations to determine the nature of the interaction. Linear contrasts were performed to obtain overall P values for variables with >2 categories. Associations were considered statistically significant at the level of P ≤ .05.
Population Characteristics
A total of 114,845 patients in the CDW met the age and health care visit criteria for our study. Of these, 17,360 (15.1%) had been diagnosed with DM. Compared with patients who had not been diagnosed with DM, patients with DM were older (mean, 59.4 years [SD, 13.7 years] vs 47 years [15.7 years]). Patients with DM were also more likely to be male (42.8% vs 34.8%) and more likely to be non-Hispanic Asian American, Native Hawaiian, or other Pacific Islander (42.9% vs 35.7%) (Table 2). In addition, more patients with DM than those without were insured through Medicare (13.4% vs 5.6%) and fewer had private insurance (4.2% vs 11.7%) (data not shown).
Characteristics of Adult Community Health Applied Research Network Patients with at Least One Primary Care Visit per Year, for at Least 3 Consecutive Years, between January 1, 2006, and December 31, 2013 (N = 114,845)
Among the 17,360 patients who had been diagnosed with DM, 13,790 (79.4%) had DM only, 817 (4.7%) had DM + CVD, 2,495 (14.3%) had DM + DEP, and 258 (1.5%) had DM + CVD + DEP (Table 3). The mean (SD) age at the index date for each group was 54.7 (13.4), 61.9 (12.5), 50.7 (12.2), and 58.2 years (10.7 years), respectively. The 2 groups of patients with depression (DM + DEP or DM + CVD + DEP) had a noticeably higher percentages of women and non-Hispanic whites than the groups with DM only or DM + CVD, whereas the latter groups had larger percentages of non-Hispanic Asian American, Native Hawaiian, or other Pacific Islander. The DM-only group had the largest percentage of patients whose primary language was not English and the smallest percentage of patients with ≥5 annual visits. The 2 groups with CVD (DM + CVD or DM + CVD + DEP) had larger percentages of patients who were continuously insured (Table 3).
Characteristics of Adult Patients with Diabetes in the Community Health Applied Research Network Who Had at Least One Primary Care Visit over at Least 3 Consecutive Years
Multilevel Models
Base Model
Because of missing records for some measures, the sample size was reduced to 14,878 patients from 14 CHCs for the multilevel models. Eight percent of patients were missing HbA1c levels, 3% were missing primary language, and 2% were missing insurance status.
Associations with Index Date
After adjusting for age, sex, Charlson score, race/ethnicity, and node, the estimated marginal mean HbA1c level differed significantly across the 4 DM groups at the index date (P < .001 overall) (Table 4). Contrary to our hypothesis, compared with the DM-only group, mean HbA1c level tended to be lower for the groups with diagnosed depression (DM + DEP and DM + CVD + DEP groups) at the index date. We also found an association between mean HbA1c level and primary language (P = .009), such that Chinese-speaking patients had a lower HbA1c level than did English speakers (β = −0.213; P = .028). In addition, compared with patients who were continuously insured (P = .002 overall), those who were continuously uninsured (β = 0.375; P = .010) or who had gaps in insurance (β = 0.133; P = .022) tended to have higher mean HbA1c levels at the index date (Figure 2A, Table 4). HbA1c level at the index date also varied significantly by race/ethnicity (P < .001 overall); non-Hispanic African Americans (β = 0.314; P = .004) and those in the "other" category (including American Indians and those who identified as multiracial; β = 0.393; P = .009) experiencing higher mean HbA1c compared with non-Hispanic whites. Women tended to have lower measures at the index date than men (β = −0.231; P < .001). Unexpected index date associations included negative relationships of age (β = −0.014; P = .001) and Charlson index score (β = −0.091; P = .011) with HbA1c level.
Intercept Coefficients: Estimated Marginal Mean Differences in Average HbA1c Level at the Index Date*
Insurance status. Baseline interaction of HbA1c with insurance status and diabetes status in the diabetes mellitus [DM] + cardiovascular disease [CVD] + depression [DEP] group (A) compared with DM-only group (B). For the DM + CVD + DEP group, baseline HbA1c did not differ among insurance groups, whereas for those with DM only, uninsured patients had higher baseline HbA1c.
Trajectory Associations
The trajectory (slope) of the HbA1c level differed by insurance status (P = .014 overall) (Table 5). Patients who were continuously uninsured had steeper increasing trajectories than did those who were continuously insured (β = 0.054; P = .024). Primary language did not reach statistical significance (P = .052), but Chinese-speaking patients had a more positive slope on HbA1c level than did English speakers (β = 0.039; P = .051), meaning that HbA1c level increased more rapidly for Chinese-speaking patients over time (Table 5). Among the adjustor variables, older age was associated with a decreased trajectory of HbA1c level compared with younger patients (β = −0.003; P < .001). Model results indicated that there is still significant unexplained variability relating to both the index date and trajectories after accounting for the variables in the model.
Slope Coefficients: Estimated Marginal Mean Differences in Average Change or Slope (Trajectory) of HbA1c Level for Every Unit Change in the Variable*
Interaction Models
Because of the complexity of the multilevel models, we fit 2 separate interaction models to evaluate potential interactions of insurance status and primary language with DM status. Each interaction term was evaluated separately by adding it to the base model described above.
We found a statistically significant interaction between insurance status and DM status (P = .024 overall): there was a significant uninsured versus insured vector for DM + DEP versus DM-only interaction (β = −0.441; P = .008). HbA1c at the index date did not differ among patients with DM + DEP between those who were uninsured and those who were insured; however, patients with DM only who were uninsured tended to have higher HbA1c at the index date compared with those who were insured (Figure 2).
In addition, the interaction of primary language with DM status was significant for both index date and trajectories of mean HbA1c level. There was a significant Spanish versus English language vector for DM + DEP versus DM-only vector interaction at the index date (β = −0.285; P = .008): for patients in the DM + DEP group, those who spoke Spanish as their primary language had a lower HbA1c value at the index date than did those who spoke English as their primary language, but for patients in the DM-only group, the HbA1c value at the index among those who spoke Spanish as their primary language did not seem to differ that of those who spoke English (Figure 3). There was also a significant Spanish versus English language vector for the DM + CVD versus DM-only interaction on the time trajectory (β = −0.171; P = .016). Among those who spoke Spanish as their primary language, HbA1c declined more over time in the DM + CVD group than in the DM-only group. Moreover, among those who spoke English as their primary language, HbA1c level appeared to increase more quickly in the DM + CVD group than in the DM-only group; that is, the differential in the positive and negative trajectories over time was larger in the DM + CVD group than in the DM-only group for English and Spanish speakers, respectively.
Interactions of HbA1c with primary language and diabetes status in the diabetes mellitus [DM]–only group (A), the DM + cardiovascular disease [CVD] group (B), and the DM + CVD + depression [DEP] group (C). Among those for whom Spanish was the primary language, HbA1c declined over time to a greater degree in the DM + CVD (B) group compared to the DM-only group (A). For the DM + CVD + DEP group (C), Spanish speakers had lower baseline HbA1c than English speakers compared with the DM-only group (A), in which a baseline difference was not detected between English and Spanish speakers.
For both interaction models, coefficient estimates and significance levels of the main effects and adjustor variables remained consistent with those estimated in the base model.
To our knowledge, this is the first published study to examine the relationships among SDHs, DM, CVD, and depression in a large national cohort of safety-net CHC patients. Our results demonstrated that 2 SDH factors, primary language and insurance status, were associated with HbA1c level among adult patients with at least annual visits to CHCs over a 3-year period and that these associations varied with disease status, although not always in the directions hypothesized. Our results also indicated that, after accounting for these SDH factors and adjusting for CHC, node, and traditional demographic measures, there was still significant unexplained variability relating to both baseline HbA1c measures and trajectories.
In this study continuously uninsured patients and those with insurance gaps tended to have higher mean HbA1c levels at the index date, and trajectories over time were steeper in the uninsured group. These results are consistent with other findings suggesting that compared with insured patients, those without insurance tend to have worse access to care, less continuity of care, more unmet needs, and worse outcomes.26,27 In our analysis the interaction detected between insurance status and DM status suggests that, in CHC populations, the previous finding may depend to some extent on patients' documented comorbidities. Among patients with DM + DEP, lack of insurance was not associated with index HbA1c level as it was in the DM-only group. One interpretation is that more complex patients are followed more closely by providers regardless of insurance status; these patients may also be more proactive in seeking care and complying with therapy in the CHC setting. Alternatively, it may be that uninsured patients in the DM-only group had greater frequencies of undetected/undocumented comorbid disease (thus not accounted for in the Charlson index score), or had more advanced DM at the index date. Overall, these findings support the notion that continued efforts are needed to identify, describe, and address disparities in and access to insurance coverage.
We detected multiple associations between HbA1c level, primary language, and DM status. This is consistent with previous studies that primary language is associated with receipt of recommended health care34,35 and that language barriers contribute to health disparities in patients with DM.36 Our findings may also reflect the influences of cultural, genetic, or socioeconomic factors and/or health care access (or lack thereof). For example, among Chinese speakers, we saw a lower index HbA1c value and a steeper (positive) trajectory over time. Previous studies indicate that Asian Americans are at higher risk for DM than their white counterparts.37,38 Our results may suggest that the trajectory of the disease could potentially be more aggressive in this population, which may warrant more diligent monitoring. Such monitoring could include assessment of medications adherence or cultural perceptions/acceptance regarding available therapies.39 Protective associations noted for speakers with limited English proficiency compared with English speakers in the more complex disease groups compared with the DM-only group (ie, HbA1c at the index date in the DM + DEP group and trajectory differential for the DM + CVD group) may reflect more intensive monitoring or increased access to and effectiveness of culturally appropriate enabling services.
We also found some unexpected associations: both groups with depression diagnoses (DM + DEP and DM + CDV + DEP) had a lower HbA1c at the index date compared with the DM-only group; older age was associated with a lower HbA1c at the index date and a declining trajectory over time; and lower Charlson index scores were associated with steeper increasing trajectories, contradicting earlier studies that higher HbA1c is associated with depression,40,41 older age,42 and more comorbidities.43 Similar findings have been observed previously in CHC settings,44 however, and are consistent with CHC missions of enhanced monitoring and/or health care access for safety-net patients with high comorbidity burdens. CHCs prioritize patients with greater disease complexity and/or more social needs and provide enhanced education and enabling services, as well as outreach, in an effort to ensure that these patients receive appropriate care and take the necessary steps to manage their condition. For example, CHCs that are recognized as patient-centered medical homes are required to identify a chronic condition for targeted improvement. A CHC patient-centered medical home focusing on DM care coordination may refer patients with HbA1c >8% to care coordination services, which may result in improved health outcomes compared with patients not receiving services.45 In addition, more complex patients may have more visits,46 providing more opportunities for HbA1c monitoring and intervention, or providers may view controlling HbA1c as a more urgent priority in patients with multiple comorbidities. Services and treatments received for the comorbidities could also have contributed to the stability of and adherence to DM treatment, even when the use of DM-related services was comparable.47 In addition, patients in the more complex DM groups, because of their disease status, could have been receiving more social support from their family, friends, and community, which contributes to better monitoring of their conditions. Another possibility is that, relative to other groups, the DM-only group may have been earlier in the identification/trajectory of disease and therefore less likely to have established stable treatment and monitoring patterns and/or tended to have additional undetected or undocumented health conditions.
Our results should be interpreted with caution because of the observational nature of the study and the variability in EHR documentation/coding practices across multiple systems. In addition, our results relate only to patients who had ≥1 visits for at least 3 consecutive years in a CHC ambulatory setting. Our results may not apply to patients who do not meet these criteria or who may be more vulnerable to poor health-related outcomes because of more social, clinical, economical, and logistic barriers than the patients studied. In addition, there are likely unmeasured covariates or confounders (including other SDHs) that may influence HbA1c. For example, we were unable to account for potentially important SDHs such as health literacy, housing status, and domestic violence. These unmeasured SDHs could have contributed to some of the unexplained variance in the models. The relationships between the unmeasured SDHs and the SDH variables in this study (insurance status and primary language) may also influence a patient's disease status. In addition, our use of the Charlson index score to adjust for comorbidity burden does not account for conditions that are not predictive of mortality but do affect, for example, quality of life or physical functioning. Finally, there were relatively small numbers of patients in certain groups (eg, DM + CVD, DM + CVD + DEP); our results should be confirmed in larger populations. Nevertheless, our study, conducted in a large safety-net population across 17 CHCs, demonstrates the need to better understand the interplay between SDHs, comorbidity burden, and health outcomes in vulnerable patients.
Recommendations and Future Plans
Improving population health requires collecting and examining data in a more holistic and systematic way that reflects ecosystems of patients' health-related risk factors. Understanding the relationship between SDHs and health outcomes is vital to improving outcomes for vulnerable populations facing health inequities. Reimbursement for Medicaid populations is transitioning to global payment models, and there is increasing pressure on CHC health care practitioners to document needs for enabling services. Evaluating the way SDHs affect health outcomes is critical to appropriately allocate primary care resources to patients who are most at risk and to ensure that resources are allocated to services that are most likely to affect outcomes. Standardized documentation of SDHs and services in the EHR,29 including enabling services,48,49 is especially important to carry out such evaluations to assess the intermediary role of enabling services in treating complex patients. The data on SDHs can also help in the development of new risk prediction methods that assess the contribution of SDHs to and their impact on health outcomes for all vulnerable populations at CHCs, including both older patients with comorbidities and younger patients with a single disease. By understanding the impact of SDHs on outcomes, CHCs, health plans, and other health care stakeholders can be better informed about SDHs that should be collected and included in predicting outcomes for all patients with any disease trajectory. In addition, using culturally and linguistically appropriate services for patients with limited English proficiency, which are often provided by multilingual and multicultural community health workers at CHCs, can be effective in understanding and addressing patient SDHs, especially for potentially sensitive questions (regarding, for example, incarceration and homeless status).
The goals set by HealthyPeople 2020 include eliminating health disparities and explicitly support improving the social and physical environments of populations.50 Collecting data on SDHs and accounting for these factors in analyses will contribute to reaching these goals. Until recently, however, SDHs have not been linked to clinical practice, and SDH data have not been routinely or systematically collected in the medical setting. Recognizing this gap, the Institute of Medicine of the National Academies of Science highlighted the need to integrate SDHs into EHRs to allow for a more holistic and integrated approach to care for all patients51 and to facilitate more rigorous research on the causes and consequences of health disparities and the development of interventions and policies to reduce those disparities. Future efforts should prioritize the integration of standardized SDHs into EHR systems. To meet this need, the National Association of Community Health Centers, AAPCHO, Oregon Primary Care Association, and the Institute of Alternative Futures have developed a standardized, national SDH data collection tool that was piloted across CHCs and health center networks, including those participating in CHARN. CHARN is also seeking to expand its data warehouse with additional SDHs so that future analyses will be able to account for the role of SDHs and their relationship to outcomes.
Insurance status and primary language are 2 SDHs that are associated with both index levels and trajectories of HbA1c in patients with DM who are receiving health care from a CHC on a relatively regular basis. In CHC settings these associations may vary depending on the complexity of disease burden. This study demonstrates the potential power of large, EHR-based data sets to identify patient groups at higher risk for adverse outcomes. Routine EHR documentation and standardization of SDHs, as well as enabling other ancillary services, will provide a more comprehensive understanding of the complex relationships between these measures and disease outcomes.
The authors gratefully acknowledge the following CHARN health centers and research nodes included in the study.
Association of Asian Pacific Community Health Organizations (AAPCHO), San Leandro, CA: Rosy Chang Weir, PhD, Vivian Li, MS, Gayathri Rajan, MA, and Morgan Ye; Asian Health Services, Oakland, CA: Thu Quach, PhD, MPH, Masa Tsutsumi, and Simin Kiani; Charles B. Wang Community Health Center, New York, NY: Shao-Chee Sim, PhD, Jennifer D. Lau, Ady Oster, MD, Kai Yeung, and Chihang (Ray) Yu; Waianae Coast Comprehensive Health Center, Waianae, HI: Rachelle Enos, MPH, Vija Sehgal, MD, PhD, MPH, John Williams, BBA, May Okihiro, MD, MS; Waimānalo Health Center, Waimānalo, HI: Mary Frances Oneha, APRN, PhD; University of California, Los Angeles (academic affiliate): Ninez Ponce, MPP, PhD, Marjorie Kagawa-Singer, PhD, MA, MN, RN, and Ying-Ying Meng, DrPH.
Alliance of Chicago Community Health Services, Chicago, IL: Fred Rachman, MD, Andrew Hamilton, RN, MS, Sarah Rittner, MA, Jessica Park, MPH, CHES, Elizabeth Adetoro, MPH, Anil Chitreddy, and Amro Hassan; Erie Family Health Center, Inc., Chicago, IL: David Buchanan, MD, MS, and Sara Naureckas, MD; Heartland Health Outreach, Chicago, IL: Elizabeth Salisbury Afshar, MD, MPH, and Caroline Thurlow, MD; Howard Brown Health Center, Chicago, IL: Magda Houlberg, MD, Kristin Keglovitz Baker, MS, and John Stryker, NP; Near North Community Health Center, Chicago, IL: Timothy Long, MD; North Country HealthCare, Flagstaff, AZ: Anne Newland, MD; PCC Community Wellness, Chicago, IL: Marge Altergott, PhD; Northwestern University, Chicago, IL (academic affiliate): Stephen Per sell, MD, MPH, Melissa Simon, MD, MPH, and Brian Hitsman, PhD.
Fenway Health, Boston, MA: Ken Mayer, MD, Chris Grasso, MPH, and Lydia Dant, MPH; Chase Brexton Health Care, Baltimore, MD: Sebastian Ruhs, MD, Amina Chaudhry, MD, MPH, and Mensah Amuzu; Beaufort-Jasper-Hampton Comprehensive Health Services, Ridgeland, SC: Monica Godfrey, MPH, and Jeff Ratzel; University of Washington, Seattle (academic affiliate): Heidi Crane, MD, MPH, Bill Lober, MD, MS, Justin McReynolds, and Dana King, ALM.
OCHIN, Inc., Portland, OR: Christine Nelson, PhD, RN, Jon Puro, MPA-HA, Melissa Laurie, MPH, and Jennifer DeVoe, MD, DPhil; Open Door Community Health Centers, Arcata, CA: Joseph Carroll, MD, PhD; Virginia Garcia Memorial Health Centers, Hillsboro, OR: Ann Turner, MD, and Christian Hill, MD, MPH; Multnomah County Health Department, Portland, OR: Paul Denouden, MD; Oregon Health & Science University, Portland (academic affiliate): Erika Cottrell, PhD, MPP, Steffani Bailey, PhD, Robert Schuff, MS, and John Saultz, MD; Richmond Family Medicine Clinic, Portland, OR: John Muench, MD, MPH.
Data coordinating center, Kaiser Permanente, Center for Health Research, Portland, OR: Mary Ann McBurnie, PhD, Steve Fortmann, MD, Michael Leo, PhD, Reesa Laws, BS, Phil Crawford, MS, Suzanne Gillespie, MS, Celeste Machen, Kelly Kirk, MS, Meredith Vandermeer, MPH, and Chalinya Ingphakorn.
This article was externally peer reviewed.
Funding: This Study was supported by grant no. HHSH250201400001C from the Health Resources and Services Administration.
Received for publication July 20, 2015.
Revision received March 2, 2016.
Accepted for publication March 8, 2016.
Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health. CSDH final report. Geneva: World Health Organization; 2008. Available from: http://www.who.int/social_determinants/thecommission/finalreport/en/. Accessed March 28, 2016.
Marmot M,
Bell R
. Fair society, healthy lives. Public Health. 2012;126(Suppl 1):S4–10.
Braveman P,
Egerter S,
Williams DR
. The social determinants of health: coming of age. Annu Rev Public Health 2011;32:381–98.
Adler NE,
Stewart J
, eds. The biology of disadvantage: socioeconomic status and health. New York: John Wiley & Sons; 2010.
Gottlieb L
. The social determinants of health: it's time to consider the causes of the causes. Public Health Rep 2014;129(Suppl 2):19–31.
Braveman PA,
Egerter SA,
Woolf SH,
Marks JS
. when do we know enough to recommend action on the social determinants of health? Am J Prev Med 2011;40(1 Suppl 1):S58–66.
McGinnis JM,
Foege WH
. Actual causes of death in the United States. JAMA 1993;270:2207–12.
Wilkinson R
, eds. Social determinants of health. 2nd ed. Oxford: Oxford University Press; 2006.
Hurt LS,
Ronsmans C,
Saha S
. Effects of education and other socioeconomic factors on middle age mortality in rural Bangladesh. J Epidemiol Community Health 2004;58:315–20.
Crimmins EM,
Saito Y
. Trends in healthy life expectancy in the United States, 1970–1990: gender, racial, and educational differences. Soc Sci Med 2001;52:1629–41.
Farmer P
. Infections and inequalities. Berkeley: University of California Press; 1999.
Druss BG,
Reisinger Walker E
. Mental disorders and medical comorbidity. Research synthesis report no. 21. Hoboken, NJ: The Robert Wood Johnson Foundation; 2011. Available from: http://www.rwjf.org/content/dam/farm/legacy-parents/mental-disorders-and-medical-comorbidity. Accessed March 28, 2016.
Lorant V,
Deliège D,
Eaton W,
Robert A,
Philippot P,
Ansseau M
. Socioeconomic inequalities in depression: a meta-analysis. Am J Epidemiol 2003;157:98–112.
Lett HS,
Blumenthal JA,
Babyak MA,
Strauman TJ,
Robins C,
Sherwood A
. Social support and coronary heart disease: epidemiologic evidence and implications for treatment. Psychosom Med 2005;67:869–78.
National Association of Community Health Centers (NACHC). America's health centers. Bethesda, MD: NACHC; 2013. Available from: http://www.nachc.com/client/documents/USHealthCentersFS.pdf. Accessed April 15, 2016.
Shin P,
Alvarez C,
Sharac J,
Rosenbaum S,
Van Vleet A,
Paradise J,
Garfield R
. A profile of community health center patients: implications for policy. Menlo Park, CA: Henry J. Kaiser Family Foundation; 2013. Available from: http://kff.org/medicaid/issue-brief/a-profile-of-community-health-center-patients-implications-for-policy/. Accessed March 28, 2016.
Chang Weir R,
Law H,
Frances Oneha M,
Lee SM,
Chien AT
. Impact of a P4P and HIT program to reduce emergency department hospital utilization at federally qualified health centers in Hawai'i. AAPI Nexus Policy Pract Community 2014;12(1–2):83–96.
de Groot M,
Anderson R,
Freedland KE,
Clouse RE,
Lustman PJ
. Association of depression and diabetes complications: a meta-analysis. Psychosom Med 2001;63:619–30.
Park M,
Katon WJ,
Wolf FM
. Depression and risk of mortality in individuals with diabetes: a meta-analysis and systematic review. Gen Hosp Psychiatry 2013;35:217–25.
Sturm R
. The effects of obesity, smoking, and drinking on medical problems and costs. Health Aff (Milwood) 2002;21:245–53.
Hansen MS,
Fink P,
Frydenberg M,
Oxhøj ML
. Use of health services, mental illness, and self-rated disability and health in medical inpatients. Psychosom Med 2002;4:668–75.
Li V,
Quach T,
. Building a community health center data warehouse to promote patient-centered research in the Asian American, Native Hawaiian, and Pacific Islanders population. AAPI Nexus Policy Pract Community 2014;12(1–2):.
Green AR,
Legedza ATR,
Massagli MP,
Phillips RS,
Iezzoni LI
. Interpreter services, language concordance, and health care quality: experiences of Asian Americans with limited English proficiency. J Gen Intern Med 2005;20:1050–6.
Gandhi TK,
Burstin HR,
Cook EF,
. Drug complications in outpatients. J Gen Intern Med 2000;15:149–54.
Jacobs EA,
Shepard DS,
Suaya JA,
Stone EL
. Overcoming language barriers in health care: costs and benefits of interpreter services. Am J Public Health 2004;94:866–9.
Agency for Healthcare Research and Quality. 2012 National healthcare disparities report. Rockville, MD: Agency for Healthcare Research and Quality; 2013.
Centers for Disease Control and Prevention. Vital signs: health insurance coverage and health care utilization—United States, 2006–2009 and January–March 2010. MMWR Morb Mortal Wkly Rep 2010;59:1448–54.
American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2010;33(Suppl 1):S62–9.
Laws R,
Gillespie S,
Puro J,
. The Community Health Applied Research Network (CHARN) data warehouse: a resource for patient-centered outcomes research and quality improvement in underserved, safety net populations. EGEMS (Wash DC) 2014;2:1097.
Deyo R,
Cherkin DC,
Ciol MA
. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613–9.
Bryk AS,
Raudenbush SW
. Hierarchical linear models: applications and data analysis methods. 2nd ed. Thousand Oaks, CA: Sage Publications; 2002.
Hox J
. Multilevel analysis: techniques and applications. 2nd ed. New York: Routledge; 2010.
Snijders T,
Bosker R
. Multilevel analysis: an introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage; 1999.
Cheng E,
Chen A,
Cunningham W
. Primary language and receipt of recommended health care among Hispanics in the United States. J Gen Intern Med 2007;22(Suppl 2)283–8.
Clough J,
Lee S,
Chae DH
. Barriers to health care among Asian immigrants in the United States: a traditional review. J Health Care Poor Underserved 2013;24:384–403.
Fernandez A,
Schillinger D,
Warton M,
. Language barriers, physician-patient language concordance, and glycemic control among insured Latinos with diabetes: the Diabetes Study of Northern California (DISTANCE). J Gen Intern Med 2011;26:170–6.
Brancati F,
Yeh HC
. Trends in the prevalence of type 2 diabetes in Asians versus whites: results from the United States National Health Interview Survey, 1997–2008. Diabetes Care 2011;34:353–7.
Maskarinec G,
Jacobs S,
Morimoto Y,
Chock M,
Grandinetti A,
Kolonel LN
. Disparity in diabetes risk across native Hawaiians and different Asian groups: the multiethnic cohort. Asia Pac J Public Health 2015;27:375–84.
Leung AY,
Ai Bo A,
Hsiao HY,
Wang SS,
Chi I
. Health literacy issues in the care of Chinese American immigrants with diabetes: a qualitative study. BMJ Open 2014;4:e005294.
Hassan K,
Loar R,
Anderson BJ,
Heptulla RA
. The role of socioeconomic status, depression, quality of life, and glycemic control in type 1 diabetes mellitus. J Pediatr 2006;149:526–31.
Lustman PJ,
Clouse RE
. Depression in diabetic patients: the relationship between mood and glycemic control. J Diabetes Complications 2005;19:113–22.
Kirkman MS,
Briscoe VJ,
Clark N,
. Diabetes in older adults. Diabetes Care 2012;35:2650–64.
Ahern MM,
Hendryx M
. Avoidable hospitalizations for diabetes: comorbidity risks. Dis Manag 2007;10:347–55.
Gilmer TP,
Hirota S,
Ponce NA
. Risk adjustment with social determinants of health and implications for federally qualified health centers under the Affordable Care Act. AAPI Nexus Policy Practice Community 2014;12:73–82.
Emerson HP,
Tseng W,
Chin M,
Caballero J
. Utilization of enabling services by Asian American, Native Hawaiian, and Pacific Islander patients at community health centers. Am J Public Health 2010;100:2199–205.
Dixon LB,
Kreyenbuhl JA,
Dickerson FB,
. A comparison of type 2 diabetes outcomes among persons with and without severe mental illnesses. Psychiatr Serv 2004;55:892–900.
Struijs JN,
Baan CA,
Schellevis FG,
Westert GP,
AM van den Bos G
. Comorbidity in Patients with Diabetes Mellitus: Impact on Medical Health Care Utilization. BMC Health Services Research. 2006;6:84 doi:10.1186/1472-6963-6-84.
Enabling services data collection implementation packet. San Leandro, CA: Association of Asian Pacific Community Health Organizations; 2016. Available from: http://www.aapcho.org/resources_db/enabling-services-data-collection-implementation-packet/. Accessed March 28, 2016.
Proser M
. Highlighting the role of enabling services at community health centers: collecting data to support service expansion and enhanced funding. San Leandro, CA: Association of Asian Pacific Community Health Organizations; 2010. Available from: http://www.aapcho.org/resources_db/highlighting-the-role-of-enabling-services-at-community-health-centers/. Accessed March 28, 2016.
Centers for Disease Control and Prevention (CDC). Leading health indicators development and framework. Available from: http://www.healthypeople.gov/2020/leading-health-indicators/Leading-Health-Indicators-Development-and-Framework. Accessed March 28, 2016.
Institute of Medicine. Capturing social and behavioral domains in electronic health records: phase 1. Washington, DC: National Academies Press; 2014.
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Dream of Oaxaca
Posted on April 17, 2020 by cara
My four days in Oaxaca last month are a fading dream. I was already starting to feel nervous about riding in crowded vans, as we did one day to get to the nearby ruins, and my hand sanitizer got a lot of use. But basically it was in the Before Times, when one could still move about freely, go into restaurants and shops at will, and travel on public conveyances, faces unobscured by masks.
Oaxaca, in a southerly crook of Mexico, is a lovely place, a city of fine neoclassical buildings and magnificent airy cathedrals set back on broad plazas. But the buildings that struck me most were the simple two-story attached houses, with ornate carved-stone door and window moldings, painted bright yellow, orange, blue, the sun etching sharp shadows across their fronts.
Some are used as inviting coffee shops set in hushed interior courtyards, where young folks with laptops while away the day. Some are mezcalerias, glitzy and cool or funky and old-school. Some are restaurants, many with steep staircases leading to rooftop bars.
And many are crafts shops — of varying quality, but mostly pretty high. Oaxaca has a number of specialties in that realm, including black pottery, textiles, filigreed jewelry, tinware. There are scores of shops large and small.
The city of a quarter-million centers on a zócalo, or central square. Unlike Mexico City's barren one, Oaxaca's zócalo is green and shady, thronged with people and surrounded by cafes. Street musicians, women selling textiles from bales slung over their shoulders, children playing, outdoor dance groups. It's a lively place for sure.
We stayed at Las Golondrinas ("The Swallows"), below, a hotel a little ways from the center, secluded behind a gate. The garden setting is lush, our familiar houseplants grown to Audrey II size. Outdoor breakfast under an arbor was a little disorganized the first morning, because it was a national day of support for domestic violence victims. Women were staying home, and men were cooking and serving. Chaos ensued.
I loved my pink room but for one thing: the glary light from the compact fluorescent bulbs. I spent part of the first full day in Oaxaca on a quest for warm bulbs (I'm exquisitely sensitive to bad lighting), which took me to a large electrical supply store near the central market. Once again, I surmised, the women who normally work the front counter were absent, and the men, who I'm guessing work in the back somewhere, though very kind, didn't seem to know where anything was or how to ring it up. It was adorable.
The Mercado Juarez is the phenomenal central market, bigger even than the one in Mexico City. I approached it cautiously, spending less time there than I might had I not been getting paranoid about crowds. It's everything to everybody, aisle after aisle, from meat and produce to pet lizards, bunnies and fish, bolts of floral-patterned fabric, cut-paper decorations, woven bags, colorful clothing, floor to ceiling and overflowing onto surrounding blocks.
Everything's so cheap to an American vacationer. At one of the high-end restaurants, Casa Oaxaca, it was all we could do to spend $40 apiece, including tip, on an incredible multi-course dinner — salsa made at the table and served with crisp green tacos sprinkled with cheese, a striped bass entree, a beet salad, a fancy dessert and two glasses of chardonnay from Baja's renowned wine valley. That was by far our priciest meal. Most other full-on dinners in very nice restaurants, half indoor-half outdoor, ran $16-$18. Huge portions everywhere. Sorry to go on about prices, but for me one of the chief pleasures of a Mexican vacation is not feeling stressed about overspending.
At Las Quince Letras, I had a giant squash with chocolate mole sauce (strange), and a pepper stuffed with mushrooms in a different mole. There were seven moles in all, brought on a tray for sampling. At Marco Polo, a more casual place filled with locals, it was snapper baked in a wood-fired oven and for dessert, mamey ice cream, my new favorite flavor.
The tour of Oaxaca City's botanic garden, above, on the site of the Santo Domingo monastery, is two hours long, a leisurely pace for a six-acre space. Offered in English three times a week and very popular, the official tour is the only way to visit the garden. It's all native Oaxacan plants, and naturally beautiful, of course, but not a masterpiece of garden design. It's called an ethno-botanic garden, so the aim of the tour is to tie the plants in with the native people, which was fascinating, though the guide shared way more botanical information than even I needed to know.
We easily arranged a half day small-group bus trip to Monte Alban, below, the Zapotec acropolis, through the hotel desk. That cost $15, inclusive of the services of our personable and well-spoken guide. The extraordinarily impressive site was in existence for 1,200 years, from 500 BC-800 AD. The reasons for its decline remain a mystery, but one theory is that getting water up to the elites who lived on the high ground became a problem when the peasants in the valley, who had been carrying it up there for who knows how many generations, got tired of doing so and moved on to other regions. Excavated and opened to the public only for the past couple of decades, Monte Alban is the partially reconstructed remains of a broad plateau of pyramid-shaped temples to various gods, their construction based on astounding astronomical calculations. It was good to get out of the city, which is sited in a valley, and take in the surrounding topography from on high.
Wrapping things up on our final day entailed a walk around Los Arquitos ("The Arches"), a quiet neighborhood, built around the remains of an old aqueduct, with good-quality craft stalls where artisans sell jewelry of their own design and making. Of course, I wish I had bought more. At the magnificent Museo des Culturas Oaxaca inside the Santo Domingo cathedral complex, we sought out the tomb treasures of Monte Alban, a hoard of beaded and hammered gold jewelry which rounded out the picture of the culture we'd gained from visiting the temple structures the day before.
Four days in Oaxaca was a bare minimum. A visit there could be pleasurably stretched to months. Will I ever go back? I really don't know. I'd like to. In any case, I'm glad I got there at least once.
Posted in Mexico, TRAVEL | Tagged Casa Oaxaca, ethno-botanical garden, Las Quince Letras, Los Arquitos, Los Golondrinas, Marco Polo, Mercado Juarez, Monte Alban, Museo des Culturas Oaxaca, Oaxaca | 3 Replies

redpajama_set_name: RedPajamaCommonCrawl


text:
Jon Adkins
Jonathan Scott Adkins (born August 30, 1977) is a Major League Baseball scout for the Los Angeles Dodgers. A former right-handed pitcher, the graduate of Oklahoma State was drafted by the Oakland Athletics in the ninth round of the 1998 Major League Baseball draft, and played in parts of three seasons with the Chicago White Sox (2003–2005).
Born: (1977-08-30) August 30, 1977 (age 41)
Batted: Left Threw: Right
Professional debut
MLB: August 14, 2003, for the Chicago White Sox
KBO: April 4, 2009, for the Lotte Giants
MLB: September 27, 2008, for the Cincinnati Reds
KBO: September 22, 2009, for the Lotte Giants
MLB statistics
Win–loss record
KBO statistics
Chicago White Sox (2003–2005)
San Diego Padres (2006)
New York Mets (2007)
Cincinnati Reds (2008)
Lotte Giants (2009)
In April 2006, he pitched in three games for the San Diego Padres, and was then optioned to the Padres' Triple-A affiliate, the Portland Beavers. On November 15, 2006, Adkins was traded to the New York Mets along with Ben Johnson for Heath Bell and Royce Ring. On July 28, 2007, he was called up from the minor leagues. On December 6, 2007, the Cincinnati Reds signed Adkins to a minor league contract.
With the Triple-A Louisville, Adkins served as their closer and saved 30 games (IL leader) with a 3.48 ERA. On September 9, Adkins was recalled, and pitched 1/3 of an inning and got the win. He was released in January to pitch for the Lotte Giants in the Korea Baseball Organization. He led the KBO in saves with 26 in 2009. On December 2, 2009, he signed a minor league deal with the Cincinnati Reds. On July 11, 2010, Cincinnati released Adkins. On July 29, 2010, Adkins signed with the Chicago White Sox and was assigned to Triple-A Charlotte.
As a scoutEdit
He was released by the White Sox in September 2010 and named to the amateur scouting staff of the Boston Red Sox covering the Ohio River Valley[1] in January 2011.[2] After spending five years with Boston, he was named Northeast scouting cross-checker by the Dodgers for 2016.[3]
^ Boston Red Sox
^ Eddy, Matt. "Minor League Transactions: September 15–26". BaseballAmerica.com. Baseball America. Retrieved 2 October 2010.
^ Norris, Josh, ed. (2016). Baseball America 2016 Directory. Durham, North Carolina: Baseball America. ISBN 978-1-932391-62-6.
Career statistics and player information from MLB, or ESPN, or Baseball-Reference, or Fangraphs, or The Baseball Cube, or Baseball-Reference (Minors)
Retrieved from "https://en.wikipedia.org/w/index.php?title=Jon_Adkins&oldid=896822888"


text:
USC ranked fifth in opening BCS poll
By Scott Wolf | swolf@scng.com | Daily News
October 20, 2008 at 12:00 a.m.
USC was fifth in Sunday's initial Bowl Championship Series rankings, but unlike in past years, Trojans coach Pete Carroll emerged from his usual cocoon and expressed some frustration with discussing the much-hyped ratings.
He would never judge the rankings, of course, but he showed unusual zeal explaining how little they matter to him.
"What would I be concerned about?" Carroll said. "Do you think I'm worried about winning the national championship based on the BCS?
"We play games. It's a popularity contest. It means nothing. You win all your games and see who's standing? I know you want more it but I'm not sure how it works. It's a machine. I've got no comment on who made the machines and how it works."
That is about as emotional as Carroll is likely to ever get regarding the BCS standings.
Besides, the Trojans (5-1, 3-1) can afford to take a detached view of things. USC was still picking its teeth Sunday after a 69-0 victory over Washington State.
Meanwhile, No. 1-ranked Texas must prepare for undefeated Oklahoma State (ranked sixth in the BCS) and if the Longhorns survive that, eighth-ranked Texas Tech awaits. Did we mention they just defeated Oklahoma and Missouri?
As for USC, it gets to talk up this week's road trip to mighty Arizona (5-2, 3-1), which already lost to New Mexico. This qualifies as a big game in a conference as bad as the Pacific-10.
But this might be the Trojans' biggest game of the season because it is the final contest of October. Carroll is 23-0 at USC in November, a fact not lost on the players.
"I think we're on the up-and-up and ready to peak at the right time," quarterback Mark Sanchez said. "We've got a couple games in October (one actually) and then we haven't lost in November since Coach Carroll's been here."
USC can feast on the Pac-10 morsels the rest of the way and let teams ahead of it like Alabama and Oklahoma play survival of the fittest in the two best conferences in the country.
The Trojans are not even hurt by a relatively low computer ranking of 10 th, the worst mark among the top seven teams in the BCS ratings.
"How does that happen?" Carroll joked. "Nice job."
But the human poll voters are much more respectful, with USC ranked fourth and fifth in the two top 25 polls used by the BCS.
"There are a lot of games left," Carroll said. "We've got a lot of tough games left and so does everyone else."
Carroll also addressed a minor controversy Sunday regarding the insertion of starting tailback C.J. Gable at the end of the Washington State game.
Gable scored with 2:16 remaining, which drew the ire of Cougars fans.
But Carroll claimed he had no other options.
"Broderick (Green) was gassed and we didn't have anyone else," he said. "He had to come out and missed a touchdown run."
Carroll said offensive tackle Nick Howell recorded 18 knockdowns against Washington State in his first career start. Howell will play even if starter Butch Lewis returns this week from a stomach virus. …
Among the reserves Carroll singled out against Washington State were defensive linemen Armond Armstead and Jurrell Casey, linebacker Malcolm Smith, safeties Drew McAllister and Will Harris, along with offensive linemen Derek Simmons, Tyron Smith and Thomas Herring. "Those guys showed they were ready to contribute," Carroll said.
scott.wolf@dailynews.com
USC REPORT CARD
No. 6 USC 69, Washington State 0
RECORD: 5-1, 3-1
WEEK 6 GPA: B
Mark Sanchez never got pressured in a practice-like atmosphere.
USC also had three 100-yard rushers against Occidental in 1929.
And Occidental might have been better than Washington State.
Reserve Nick Howell had 18 knockdowns in first career start.
Freshman Armond Armstead quickly emerging as a contributor.
Malcolm Smith and Chris Galippo saw real action against fake team.
What did they have to do all day with Cougars refusing to pass?
That missed extra point might matter in a respectable game.
At least Pete Carroll substituted in second half to avoid 100-point margin.
Scott Wolf | reporter
Scott Wolf covers USC and the Pac-12 Conference for the Southern California News Group.
swolf@scng.com
Follow Scott Wolf @insideusc
UCLA gymnastics unified for home opener against Oregon State


text:
Kshay (Hindi: क्षय, Corrode) is a 2012 black-and-white independent Indian psychological drama film written and directed by Karan Gour. The film premiered on 8 October 2011 at the Chicago International Film Festival. It went on to win several international awards like the Jury Award at the Indian Film Festival of Los Angeles. It was released in theatres in India on 15 June 2012.

Plot
A middle-class Indian housewife, Chhaya (Rasika Dugal) becomes obsessed with an expensive sculpture of the goddess Lakshmi and is determined to possess it despite the fact that she and her husband Arvind (Alekh Sangal) have little money.

Cast
Rasika Dugal as Chhaya
Alekh Sangal as Arvind
Sudhir Pednekar as Bapu
Adityavardhan Gupta as Sculptor
Nitika Anand as Shruti
Ashwin Baluja as Jamil
Siddharth Bhatia as Amay
Shalini Gupta as Woman in Market
Abhinay Khoparzi as Man on the Bus
Madhav as Construction Worker
Asit Redij as Asif
Shazeb Shaikh as Electrician

Production
The film was shot in Bhayandar, Mumbai on a low budget of  over a period of four years, with a director and one member crew. The director Karan Gour used HDV and shot the film in color, later changing it to black and white in post-production.

Release and reception
After its premiere on 8 October 2011 at the Chicago International Film Festival, the film travelled to festivals like Indian Film Festival of Los Angeles, and South Asian International Film Festival. The film later saw a limited release at PVR Cinemas multiplexes starting 15 June 2012, followed by a DVD release. In its year end review, Mint called the film an "assured debut" while adding the film to the "top films of 2012".

Awards
2012 Jury Award for Best Feature at the Indian Film Festival of Los Angeles
2012 Asian New Talent Award for Best Film at the Shanghai International Film Festival

References

External links
 Kshay (Corrode), website

2010s Hindi-language films
Indian black-and-white films
Indian independent films
2010s psychological drama films
Films shot in Maharashtra
2012 directorial debut films
Indian psychological drama films
Indian avant-garde and experimental films
2010s avant-garde and experimental films
2012 independent films
2012 films

redpajama_set_name: RedPajamaWikipedia


What's The Verdict: The Weinstein Company Using Genius Products as a Personal Bank Account?
This past Thursday, a trustee for a bankrupt home video company, Genius Products, has filed a multi-million dollar multi-claim lawsuit against The Weinstein Company, with accusations of perpetrating a fraud scheme and demanding the return of more than $130 million! It was in 2011 that Genius Products, LLC was forced into involuntary bankruptcy by World Wrestling Entertainment and two other companies over $8.5 mill in debt. According to the newest complaint from Chapter 7 trustee Alfred Siegel, by 2011 Genius Products was already in debt thanks to "onerous and one-sided" agreements forced upon it by The Weinstein Company. Essentially, The Weinstein Company is being accused of treating Genius as its personal bank account and transforming ESPN, Discovery and Genius' other licensors into unsecured creditors.
The attorney for The Weinstein Company says his client lists tens of millions on Genius and that Genius' story does not align with the real story. So, what is the story?
Weinstein formed Genius in 2005 gaining the exclusive rights to distribute Weinstein home videos in the US. The following year, 2006, Genius would become an independent company and in accordance with an operating agreement, TWC held onto 70 percent. Supposedly, becoming an independent company was a sham to mislead third parties, where Weinstein would have full control over Genius and distribution agreements that made it impossible for Genius to operate profitably. Another way in which Genius was allegedly disadvantaged by TWC is the requirement that it keep about 40 employees exclusively working on Weinstein titles at a cost of $2 mill per year. Despite an exclusive right to distribute, Genius was not permitted to distribute in Blu-Ray and other new formats.
All in all, it looks like The Weinstein Company dictated the agreement terms in a way that pretty much all the revenue from the home video distribution was transferred to Weinstein and all expenses were paid the 'Debtor' or Genius Products. This is all said to have gone on for about 2 years, up until 2008 when Genius was seriously running out of money. What happened next is that Weinstein transferred its majority ownership to an investment firm, Quadrant Management, Inc. as a means of removing itself from the relationship as a minimal cost.
While Genius Products claims this was a major fraud scheme, TWC claims that they were actually the victim in this whole series of events. As litigation proceeds, there will be more to come, so, stay tuned on What's The Verdict for updates!
You are currently browsing the archives for the Fraud category.


text:
import pytest
from thefuck.shells import Powershell

@pytest.mark.usefixtures('isfile', 'no_memoize', 'no_cache')
class TestPowershell(object):
    @pytest.fixture
    def shell(self):
        return Powershell()

    def test_and_(self, shell):
        assert shell.and_('ls', 'cd') == '(ls) -and (cd)'

    def test_app_alias(self, shell):
        assert 'function fuck' in shell.app_alias('fuck')
        assert 'function FUCK' in shell.app_alias('FUCK')
        assert 'thefuck' in shell.app_alias('fuck')

    def test_how_to_configure(self, shell):
        assert not shell.how_to_configure().can_configure_automatically

redpajama_set_name: RedPajamaGithub


text:
Gaming Realm
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Discussion Central
By Various_Files, September 22, 2018 in Gaming Realm
Various_Files 0
Let's talk about the latest Resident Evil 2 Remake trailer "Resident Evil 2 Remake - Official Story Trailer | TGS 2018".
https://www.youtube.com/watch?v=qSzV4R9GQGg
What is everyone's thoughts on this trailer?
I think it looks amazing to tell you the truth. Lots of changes kind of fit each character.
Koby 17,508
🎇Rise like the Phoenix🎇
It's the most hyped game atm for me. I'm so wanting to play as soon it as is released.
Same here. It's got me hyped on so many levels I hardly care for any other game.
Llam 0
Kind of interested after what they did with 7, but at the same time thinking about doing what I did with 7 and waiting, then getting the deluxe edition when the price drops. Hoping it's not as much escort missions as it's looking.
Also in mourning that we won't get a SH 2 remake.
6 minutes ago, Llam said:
I actually didn't like RE7 for a couple reasons. Never was a fan of first person perspective in video games and even with the shaking or w/e they called it in the settings turned off... I still couldn't play the game for more than 10-15 minutes before feeling motion sickness and having to take a break just from the way the camera swirled when you moved around or adjusted the camera. In terms of gameplay in all of the Resident Evil games thus far, my favorite would definitely be Revelations 2. Luckily for me the RE2 remake seems to be taking on a lot of aspects of that game which have me even more excited to revisit the classic story lines. Shoot this even got me into replaying RE0 HD some last month lol.
coldkestrel 4
Hedge Knight
Very interesting... Remakes can sometimes be iffy but this looks worth checking out
On 9/23/2018 at 6:36 AM, coldkestrel said:
It one of the few remakes that I do find worthy enough to check out.
On 9/22/2018 at 1:35 PM, Llam said:
I actually haven't played the seventh one yet. Is it any good? It was something I thought about getting but waiting till like a Complete Edition came out or something along the lines down the road.
Mod Edit: Please use the multi-quote button rather than double post.
40 minutes ago, Various_Files said:
Let's hope it turns out as good as it looks.
https://imgur.com/a/kb0su5d#wpvrGhE
Picked up my pre-ordered copy from BestBuy today for PS4. It said I was suppose to get two exclusive weapons for preordering from Bestbuy but I apparently didn't. Don't see any mention of them anywhere. I didn't bother with spending $10 more for the Deluxe. I don't care about alternate costumes... so the only thing that interested me was possibly replaying later with the "classic soundtrack".
Looks amazing and feels much different than I expected. Only played a little today as Leon and he just moves very slowly. Even when you make him run, he only moves slightly faster than what you'd expect him to be during walking. They definitely wanted to make it less action-y and thus slowed the pace.
Zombies don't seem to want to go down. I head shot the first zombie in the gas station 7 times and he was still moving. With one bullet left I had to just run past everything instead of trying to fight. So much more reminiscent of older Resident Evil where you had to be sparing with ammo and not just kill everything that moves
DenDuval 18
So other than Koby, does anyone else think the game is good. I'm so afraid the game has been overhyped that it will not be able to meet expectations.
9 minutes ago, DenDuval said:
that moment when you realize you're opinion doesn't count....
Okay I'll go cry in a corner now.
Nah, but unfortunately I'm gonna be too busy to play anymore this weekend.
Sheesh, sorry Koby. I was just fishing for other opinions; review by consensus, before buying the game. Didn't mean to be so harsh to you.
I am super excited for the game and simultaneously super anxious that it would suck that I'm like a deer in the headlights about it.
I'm stuck.
Plus it's so new that there has been no reviews to qualitate the product. So yeah.. I'm boned.
Edited January 26, 2019 by DenDuval
More to say
I was just kidding. Figured the emoji's made that clear. No worries. lol.
Kid Boruto 1,881
Playful Explorer
I somehow forgot I had pre-ordered the game, my copy arrived yesterday and I'm probably gonna end up returning it.
Sadly don't have the time between catching up on Kingdom Hearts and starting the Devil May Cry series.
On 1/26/2019 at 1:34 PM, Kid Boruto said:
Been playing a little DmC in the last few months (Steam version). I beat Devil May Cry 1-3 back in the day and stalled out on the 4th. I'll play the 5th eventually, but it's not something I'm going to pre-order. I'll buy it when it hits a big sale for at least 50% off or more from the initial price.
Still have so many games I need to play but so little time. Gonna be a while before I'm ready to play KH3 because I still wanna play some of the side-games.... and yet I have many other games higher priority, such as Resident Evil 2 lol.
† Irish 31
#COOKIES!!!!
† Retired
RE2 Remake has been a blast for me! I've always been a massive fan and the game takes the knowledge of the original game and throws it out the window. It is a fantastic game that I would recommend to anyone. Limited resources, close encounters, and the boss fights are amazing.
inuyasha211 17
Crusaders +
i'm only 3 hours into Leon's campaign, really enjoying this remake
Beat Leon's campaign in about 7 hours.
It unlocked their classic costumes as an alternative and said I could play New Game 2nd Run with Claire. So guess that's next on the agenda.
Not sure what the difference is in playing a New Game as Claire or selecting the New Game 2nd run, but I suppose I'll eventually find out.
Since my last post, I've beat Claire's 1st run. As well as finished Leon's 2nd Run and the 4th Nightmare Extra where you play as Hunk.
So all that's left for me is Claire's 2nd Run before the new free DLC this Friday.
Leon's 2nd run was a bit different at the beginning as to how he ends up in the station and what order he goes through it, but then the sewers & lab was identical to the first run. The only difference after the station that I noticed anyways was that there was
an additional boss fight and another cutscene past where the 1st run ended.
Claire's 1st run and Leon's 2nd run were the ones considered canon in the original version. Although they kind of threw some parts into both that otherwise logically do not make the two fit together.
Like for example:
In Claire's 1st run Birkin takes out Tyrant earlier in the game, yet Leon still fights Tyrant in the end. And the fact that both Leon and Claire have to fight Birkin at the same point in the lab, which obviously wouldn't make sense if you merged the two runs into one coherent story.
Go To Topic Listing Gaming Realm
Referrals: gamebillet, GamersGate, Humble, Play-Asia, Voidu
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Teenage dream room photos objects hit interiors is one images from 27 genius teenage dream bedroom of Baxters Homes photos gallery. This image has dimension 600x600 Pixel and File Size 306 KB, you can click the image above to see the large or full size photo. Previous photo in the gallery is dream interior design ideas teenage girl rooms. For next photo in the gallery is purple luxury bedroom designs home decor. You are viewing image #26 of 27, you can see the complete gallery at the bottom below.


Nothing comes for free. True in business and often true in biology.
The pleiotropic function of TEP1 genes in mosquito immunity and reproduction suggests that there is a significant cost to male reproductive success associated with enhanced female parasite resistance.
Even in the simplest of organisms, helpful traits often come at the cost of others. These relationships may be the result of resource allocation. For example, a bacteria might use energy to pump an antibiotic drug out of the cell at the expense of replication. They can also be the result of physiological constraints, as when an immune pathway results in increased production of molecules that cause oxidative damage and shorten lifespan. A particularly interesting form of trade-off relates to the very genetic architecture of an organism. Antagonistic pleiotrophy occurs when a single gene controls more than one trait. One or more of these traits increases overall fitness and one or more of them decreases overall fitness.
It now looks as though an antagonistic pleiotrophy may in part explain the maintenance of malaria susceptibility in mosquito populations. Theioester- containing proteins (TEPs) are postulated to function in the insect immune response in a manner analogous to the function of complement factors in vertebrates. They mark pathogens for the immune system. Marked cells are then destroyed by other components of the immune response (phagocytosis or cell lysis).
Exploration of the Anopheles genome has revealed that these mosquitoes have several TEPs. One in particular, TEP1 has been implicated in the mosquito immune response to several pathogens including malaria parasites. This protein is secreted into the mosquito circulatory system in response to wounding or pathogen exposure.
When malaria gametocytes are taken up into the mosquito with a blood meal they undergo a sequence of events culminating in sexual reproduction. The product of gamete fusion is a motile ookinete.
In lines where researchers have knocked out TEP1, females end up with 5 times more malaria parasites successfully developing in the midgut than controls.
What has always seemed a bit strange is that there are four allelic classes or forms of TEP1 in natural populations of mosquitoes. Some of these confer resistance to malaria and others render the mosquito susceptible. Why don't all females carry the resistant forms of TEP1? It seems in a world filled with pathogens that having daughters best equipped to fight infection would be heavily selected for.
Pompon and Levashina recently asked a fantastic question. What about the sons that carry this form of TEP1? They looked for TEP1 in males using florescent tags and they found it, but in a very surprising place: the testes. By looking at the presence of TEP1 over the course of male development and in males just after mating, they narrowed this association down to developing sperm cells. What was a protein involved with targeting pathogens doing in the testes binding to sperm?
Anopheles male accessory glands and testes. Taken from MRH Handbook in Anopheles research.
Based on their work on the female immune response, Pompon and Levashina hypothesized that TEP1 might target defective sperm in the male testes in the same way it targets parasites in the female midgut. They found that damaging sperm using radiation increased the proportion of TEP1 tagged sperm cells. When normal males were exposed to radiation the proportion of damaged sperm cells decreased with time. In males in which TEP1 was knocked down, the proportion remained high over time. This accumulation of dead sperm had real life consequences. When males with high proportions of dead sperm mated, their eggs had significantly lower hatching rates.
These experiments suggested that TEP1 played an important role in male fertility, but the evidence came from males exposed to radiation, hardly a natural state for a male mosquito. To determine how TEP1 might be influencing male fertility in more "normal" conditions, the researchers looked at defective sperm and fertility in lines carrying the different naturally occurring allelic classes of TEP1. Males carrying resistant alleles where more likely to have defective sperm accumulate than susceptible males. Again, this accumulation decreased hatching rates. The offspring of males carrying resistant forms had signifncantly lower hatching rates than those of susceptible males. In other words, while resistant parents may produce daughters with greater resistance to malaria, they also produce sons that are more likely to "shoot blanks".
Pleiotropic function of TEP1 genes in immunity and reproduction likely contributes to the maintenance of susceptible alleles in natural populations. This suggests that there is a significant cost to male reproductive success associated with enhanced female parasite resistance. Several new control strategies are under development which involve genetically engineering malaria resistant mosquito strains (population replacement). This involves manipulating traits contributing to resistance in the laboratory and releasing males into the field. Ideally, we would release males that will produce daughters that resist malaria infection and sons that are competitive for wild female mates. This study highlights that the interconnectedness of reproduction and immunity should be considered in the creation of these lines.
Lauren is a Lecturer the Silwood Park Campus of Imperial College London. She is broadly interested in the behavioural ecology of medically important mosquitoes. Current research interests include sexual selection in mosquito swarms, the effect of infection on vector behaviour, and how incorporating vector behaviour into our understanding of transmission dynamics will improve our ability to understand and control disease.


As a condition of your child's participation in any of our sports programs please read the following Parent Sportsmanship Pledge.
1. It is often diffficult for parents to see their children in situations where they are being evaluated, especially at a very young age. But if we spend our time worrying too much about how they're doing, we are missing what should be a wonderful experience for us as well as them. If they're not great players, we will love them anyway for who they are, find reasons to praise them for their accomplishments on the field, big or small, and know that they will be great at other things.
2. The game is for the kids. We shouldn't let our natural competitive spirit and normal desire for glory turn into angry yelling at the kids when they don't fulfill our expectations. Sometimes our expectations may be unrealistic. If they're having fun as well as gaining confidence by learning skills, and being coached by a person who is teaching and mentoring without anger, that should satisfy us.
3. Winning is one goal, but only one of many goals. Fewer than half the teams competing win their games on any given day (counting ties) and as studies have consistently indicated, children play mainly for the fun of it. Kids almost unanimously rate the FUN they have during the match much higher than the score at that single moment when the game is over.
4. We need to also remember that it is okay to lose because life is not just about winning. We suffer setbacks at times. Learning to accept defeat gracefully and come back enthusiastically next time is an important lesson to teach. As long as children try their best, they are winners on another level even when they lose the game.
5. Coaches and referees deserve RESPECT. They make mistakes as we all do, but whether they're great at what they're doing or not, we have a responsibility to set a good example to help teach RESPECT to our children. Yelling criticism at referees, coaches or players is inappropriate and that behavior must be subject to penalties from the club, including the last thing the club wants to consider, possible suspension from attending matches. Children often reveal that they are embarrassed when their parents shout out criticism. Referees do not change their calls but many do decide to quit because of the abuse they take from parents, coaches and players. Yet we wonder why we don't have enough referees. Coaches are giving time and effort and if they are making it a positive FUN experience, we should RESPECT them for that.
6. Some of us are more emotional, which is good in some ways. Some of us are calmer, which also is good in some ways. When we are emotional, perhaps sometimes because of frustrations in other parts of our lives, it is harder to keep ourselves under control. It can become a problem in this setting when we yell at the kids. When they think we're angry, it can make them nervous and lower their self-esteem. As a result, they may not perform as well as they can. Sometimes we don't even realize how angry we sound because, for one thing, our children don't always talk about their feelings. They may tell someone else.
7. When someone makes unsportsmanlike comments to other spectators, children, coaches, or referees, it is often best to ignore them unless they're good friends. In general, when someone is excited and negative, that is NOT the time to talk about sportsmanship. We should avoid confrontations at all costs.
8. Even if our child has outstanding ability, we shouldn't look too far into the future. We need to be realistic about the the question of whether they will get a lot of playing time in high school or college. And considering the rapidly increasing number of good players coming out of high school, we need to be aware there are relatively few college soccer scholarships and competition for spots on the roster will be intense.
9. Know the level of commitment expected and the policy on playing time. If you have any questions, it is often best to ask the coach during a quieter moment, not at a game.
10. If we keep the game in the proper perspective, we can realize that children usually have FUN as long as we support their effort. Putting them under pressure by being overly critical is not a good idea. If they become anxious about their performance, they may be hesitant to try new skills, they may not reach their potential and their interest in the game may decline.
11. The experience of watching children play youth sports is over all too soon. Why not relax and have fun simply knowing that the kids are having fun and we're having a good time together?


text:
Former Senate President John Russo, Ocean County Democrat leader, dies
Steph Solis
Former Senate President John Russo, a man who defined Ocean County politics for two decades, died late Saturday night after battling an illness. He was 84.
Russo, a Democrat, was arguably Ocean County's most powerful politician, rising to the ranks of Senate president in 1973 and launching an unsuccessful bid for governor in 1985. He served five terms in the state Senate before stepping down in 1991.
"Ocean County lost a legend last night," said Russell Corby, who worked as Russo's director of legislative services.
Russo, who served 18 years in the state Senate, was known as a law-and-order legislator. He was known as the father of the state's death penalty law and sponsored the state's ban on assault weapons. In an interview with the Press in 1991, Russo said his greatest regret was never getting a ban on handguns passed in the state Senate.
"Hopefully, I leave - not with any statues in the park, but just maybe with a feeling of respect that I did the best I could," Russo told the Press at the time.
Russo was also the architect behind New Jersey's cap on state spending, the first such law in the nation. He cited that as one of his two proudest achievements in the Legislature; the second was a law that exempted senior citizens from paying income tax on their first $10,000 in earnings.
As a Democrat, Russo defied the odds, winning five terms in heavily Republican Ocean County. Once Russo left the scene — with the exception of the 1991 legislative race to succeed him — Democrats have never again won a statewide race in Ocean County.
Former Assemblyman John Paul Doyle was Russo's running mate and served with him for 18 years.
"It's kind of hard to think that there ever were Democrats elected to more than a municipal office in Ocean County," he said. "In 1973, the stars were aligned."
Doyle and Daniel Newman ran for the General Assembly with Russo in 1973, forming the Russo-Newman-Doyle trio. They created a joint constituent services office so residents had one place to go to meet their legislators.
Russo led the operation, determined to work 110 percent, Doyle said. He reached out to constituents and knew how to connect with the working man.
"He had an appreciation for the average guy. He was raised poor," Doyle said. "He said he never could have appreciated what he was able to do and accomplish if he had never started where he did in Asbury Park."
Politicians across the state expressed their condolences. Senate President Steve Sweeney called Russo a strong leader within the state Senate.
"Senate President Russo may have left public office, but he remained engaged in public policies and continued to share his experience and expertise with me during my time as Senate president," he said. "New Jersey is a better state because of John Russo."
Senate Republican Leader Tom Kean said Russo was "a dedicated public servant, a loving father, and a leader committed to improving New Jersey for all its residents."
Senator Raymond Lesniak, a Democrat representing the 20th district, played golf and watched Notre Dame football with Russo. He said Russo's temperament and wisdom are rare in present-day politics.
"Senator Russo was a special person for me," Lesniak wrote in a statement. "A friend to all, Democrats and Republicans, John stood on his core beliefs regardless of politics."
The lobbying firm Princeton Public Affairs Group, where he worked after his legislative career, also lauded Russo's demeanor in the statehouse.
"I hesitate to call John a 'throwback' when partisanship stayed in the house and you could 'break bread' together after the day's work," said Dale Florio, PPAG founder. "To us, John was and will always be an example of how those of us who engage in the science of politics should practice our craft."
While in the Senate, Russo authored more than 200 laws.In addition to serving as Senate President and acting governor from 1986 to 1990, he also served as Senate majority leader. He was elected Ocean County Democratic chairman for four terms in the 1970s.
Russo's record did have its blemishes: He came under fire after accounts surfaced about several family members being on the state payroll and junkets to out-of-state conventions.
But in a 1991 interview, Russo decried what he called the "feeding frenzy" of negative stories about politicians, although he conceded that he probably made a mistake when he helped his family get jobs with the state.
What he objected to about the negative publicity, Russo said, was that it created an impression "that he was in it for what he could get."
What he was in politics for, he said, was ego. "There's nothing wrong with that. Ego runs this world," Russo said. "If you don't have ego, you don't have senators. Being called a senator is a wonderful thing."
In addition to his political career, Russo was also an active pilot, skier and golfer, Corby said. "He was a very, very active guy," he recalled.
Corby said he didn't have details on Russo's illness but said the former lawmaker had been sick for a while before his death.
Russo's family was also deeply involved in Ocean County government. His son, John Russo Jr., is the former mayor of Toms River and a Superior Court judge in the county. His daughter, Caryl Lynn Russo, married state Sen. Robert Singer, a Republican who represents the 30th district in Ocean County, which includes Lakewood.
Russo was raised by an Italian family in Asbury Park. He went to Notre Dame University in Indiana before getting his law degree from Columbia University in New York. He spent nine years as an assistant prosecutor for Ocean County before launching his first bid for the ninth legislative district seat in 1971.
That one he lost. The next was his first of five victories.
Corby joined Russo's second state Senate campaign. He saw Russo as a tenacious campaigner who shook hands with people and spoke with them.
"You knew there was a goal, and he wasn't just sitting home someplace. He was out there too," Corby said. "Anybody who's been in the military knows that's a sign of leadership, and he had that quality."
In 1985, Russo ran for New Jersey governor but came in second in the Democratic primary. The nominee, Peter Shapiro, lost to Republican Tom Kean.
George Gilmore, Ocean County Republican chairman, lamented that Russo never became governor. Gilmore said he would have liked to see a governor from Ocean County who understands its residents' needs.
"He was great on constituent services," Gilmore said. "The fact that he was elected as a senator in a basically Republican county, that says a lot about his tenaciousness and being attuned to what was important to the voters."
Staff writers Lisa Kruse, Erik Larsen and Bob Jordan contributed to this story.
Steph Solis: 732-403-0074; ssolis@gannett.com