Treatment discontinuation among breast, colorectal, and prostate cancer patients in Alabama

Introduction. Cancer registries provide information about treatment initiation but not the full course of treatment. In an effort to identify patient reported reasons for discontinuing cancer treatment, patients with prostate, breast, and colorectal cancer were identified from Alabama State Cancer Registry (ASCR) -Alabama Medicare linked database for interview. This study has two specific aims: (1) determine whether the ASCR-Medicare database accurately reflects patients’ treatment experiences in terms of whether they started and completed treatment when compared to patient self-report and (2) determine which patient demographic and health care system factors are related to treatment completion as defined by patient self-report. ^ Methods. The ASCR-Medicare claims dataset supplemented patient interview responses to identify treatment initiation and completion among prostate, breast, and colorectal cancer patients in Alabama from 1999-2003. Kappa statistic was used to test for concordance of treatment initiation and completion between patient self-report and Medicare claims data. Patients who reported not completing treatment were asked questions to ascertain reasons for treatment discontinuation. Logistic regression models were constructed to explore the association of patient and tumor characteristics with discontinuation of radiation and chemotherapy. ^ Results. Overall, there was a fair agreement across all cancer sites about whether one had surgery (Kappa=.382). There was fair agreement between self-report and Medicare claims data for starting radiation treatment (Kappa=.278). For starting chemotherapy there was moderate agreement (Kappa=.414). There was no agreement for completing treatment for radiation and chemotherapy between the self-report and claims data. Patients most often reported doctor’s recommendation (40% for radiation treatment and 21.4% for chemotherapy) and side effects (30% for radiation treatment and 42.8% for chemotherapy) for discontinuing treatment. Females were less likely to complete radiation than males (OR=.24, 95% CI=.11–.50). Stage I patients were more likely to drop radiation treatment than stage III patients (OR=3.34, 95% CI=1.12–9.95). Younger patients were more likely to discontinue chemotherapy than older patients (OR=2.84 95%, CI=1.08–7.69) and breast cancer patients were less likely to discontinue chemotherapy than colorectal patients (OR=.13, 95% CI=.04–.46). ^ Conclusion. This study reveals that patients recall starting treatment more accurately than completing treatment and that there are several demographic and tumor characteristics that influence treatment discontinuation. Providing patients with treatment summaries and survivorship plans can help patients their follow-up care when there are gaps in treatment recall and discontinuation of treatment.^

Pregnancy intention, intimate partner violence, and the use of prenatal care in Jordan

Previous research has suggested an association between intimate partner violence and pregnancy intention status, and pregnancy intention status and the use of prenatal care services, however much of these studies have been conducted in high income countries (HIC) rather than low and middle income countries (LMIC). The objectives of this study were to examine the relationship between pregnancy intention status and intimate partner violence, and pregnancy intention status and the use of prenatal care among ever-married women in Jordan.^ Data were collected from a nationally representative sample of women interviewed in the 2007 Jordan Demographic and Health Survey. The sample was restricted to ever-married women, 15–49 years of age, who had a live birth within the five years preceding the survey. Multivariate logistic regression analyses was used to determine the relationship between intimate partner violence and pregnancy intention status, and pregnancy intention status and the use of prenatal care services.^ Women who reported a mistimed pregnancy (PORadj 1.96, 95% CI: 1.31–2.95), as well as an unwanted pregnancy (PORadj 1.32, 95% CI: 0.80–2.18) had a higher odds of experiencing lifetime physical and/or sexual abuse compared with women reporting a wanted pregnancy. Women not initiating prenatal care by the end of the first trimester had statistically significant higher odds of reporting both a mistimed (PORadj 2.07, 95% CI: 1.55–2.77) and unwanted pregnancy (PORadj 2.36, 95% CI: 1.68–3.31), compared with women initiating care in the first trimester. Additionally, women not receiving the adequate number of prenatal care visits for their last pregnancy had a higher odds of reporting an unwanted pregnancy (PORadj 2.11, 95% CI: 1.35–3.29) and mistimed pregnancy (POR adj 1.41, 95% CI: 0.96–2.07).^ Reducing intimate partner violence may decrease the prevalence of mistimed or unwanted pregnancies, and reducing both unwanted and mistimed pregnancies may decrease the prevalence of women not receiving timely and adequate prenatal care among women in this population. Further research, particularly in LMIC, is needed regarding the determinants of unintended pregnancy and its association with intimate partner violence as well as with the use of prenatal care services. ^

Social support, health status and health services utilization among the noninstitutionalized elderly: An analysis of the Supplement on Aging to the 1984 national health interview survey

The association between Social Support, Health Status, and Health Services Utilization of the elderly, was explored based on the analysis of data from the Supplement on Aging to the National Health Interview Survey, 1984 (N = 11,497) using a modified framework of Aday and Andersen's Expanded Behavioral Model. The results suggested that Social Support as operationalized in this study was an independent determinant of the use of health services. The quantity of social activities and the use of community services were the two most consistent determinants across different types of health services use.^ The effects of social support on the use of health services were broken down into three components to facilitate explanations of the mechanisms through which social support operated. The Predisposing and Enabling component of Social Support had independent, although not uniform, effects on the use of health services. Only slight substitute effects of social support were detected. These included the substitution of the use of senior centers for longer stay in the hospital and the substitution of help with IADL problems for the use of formal home care services.^ The effect of financial support on the use of health services was found to be different for middle and low income populations. This differential effect was also found for the presence of intimate networks, the frequencies of interaction with children and the perceived availability of support among urban/rural, male/female and white/non-white subgroups.^ The study also suggested that the selection of appropriate Health Status measures should be based on the type of Health Services Utilization in which a researcher is interested. The level of physical function limitation and role activity limitation were the two most consistent predictors of the volume of physician visits, number of hospital days, and average length of stay in the hospital during the past year.^ Some alternative hypotheses were also raised and evaluated, when possible. The impacts of the complex sample design, the reliability and validity of the measures and other limitations of this analysis were also discussed. Finally, a revised framework was proposed and discussed based on the analysis. Some policy implications and suggestions for future study were also presented. ^

Infectious diseases, immune status, and health insurance among child care providers in Houston, Texas

The objectives of this study were to compare female child-care providers with female university workers and with mothers of children in child-care centers for: (1) frequency of illness and work loss days due to infectious diseases, (2) prevalence of antibodies against measles, rubella, mumps, hepatitis B, hepatitis A, chickenpox and cytomegalovirus (CMV), and (3) status regarding health insurance and job benefits.^ Subjects from twenty child-care centers and twenty randomly selected departments of a university in Houston, Texas were studied in a cross-sectional fashion.^ A cluster sample of 281 female child-care providers from randomly selected child-care centers, a cluster sample of 286 university workers from randomly selected departments and a systematic sample of 198 mothers of children from randomly selected child-care centers.^ Main outcome measures were: (1) self-reported frequency of infectious diseases and number of work-days lost due to infectious diseases; (2) presence of antibodies in blood; and (3) self-reported health insurance and job benefits.^ In comparison to university workers, child-care providers reported a higher prevalence of infectious diseases in the past 30 days; lost three times more work-days due to infectious diseases; and were more likely to have anti-core antibodies against hepatitis B (odds ratio = 3.16 95% CI 1.27-7.85) and rubella (OR 1.88, 95% CI 1.02-3.45). Child-care providers had less health insurance and job-related benefits than mothers of children attending child-care centers.^ Regulations designed to reduce transmission of vaccine and non-vaccine preventable diseases in child-care centers should be strictly enforced. In addition policies to improve health insurance and job benefits of child-care providers are urgently needed. ^

Effect of primary care availability and health insurance coverage on risk and cost of Preventable Hospitalizations in Harris County

Preventable Hospitalizations (PHs) are hospitalizations that can be avoided with appropriate and timely care in the ambulatory setting and hence are closely associated with primary care access in a community. Increased primary care availability and health insurance coverage may increase primary care access, and consequently may be significantly associated with risks and costs of PHs. Objective. To estimate the risk and cost of preventable hospitalizations (PHs); to determine the association of primary care availability and health insurance coverage with the risk and costs of PHs, first alone and then simultaneously; and finally, to estimate the impact of expansions in primary care availability and health insurance coverage on the burden of PHs among non-elderly adult residents of Harris County. Methods. The study population was residents of Harris County, age 18 to 64, who had at least one hospital discharge in a Texas hospital in 2008. The primary independent variables were availability of primary care physicians, availability of primary care safety net clinics and health insurance coverage. The primary dependent variables were PHs and associated hospitalization costs. The Texas Health Care Information Collection (THCIC) Inpatient Discharge data was used to obtain information on the number and costs of PHs in the study population. Risk of PHs in the study population, as well as average and total costs of PHs were calculated. Multivariable logistic regression models and two-step Heckman regression models with log-transformed costs were used to determine the association of primary care availability and health insurance coverage with the risk and costs of PHs respectively, while controlling for individual predisposing, enabling and need characteristics. Predicted PH risk and cost were used to calculate the predicted burden of PHs in the study population and the impact of expansions in primary care availability and health insurance coverage on the predicted burden. Results. In 2008, hospitalized non-elderly adults in Harris County had 11,313 PHs and a corresponding PH risk of 8.02%. Congestive heart failure was the most common PH. PHs imposed a total economic burden of $84 billion at an average of $7,449 per PH. Higher primary care safety net availability was significantly associated with the lower risk of PHs in the final risk model, but only in the uninsured. A unit increase in safety net availability led to a 23% decline in PH odds in the uninsured, compared to only a 4% decline in the insured. Higher primary care physician availability was associated with increased PH costs in the final cost model (β=0.0020; p<0.05). Lack of health insurance coverage increased the risk of PH, with the uninsured having 30% higher odds of PHs (OR=1.299; p<0.05), but reduced the cost of a PH by 7% (β=-0.0668; p<0.05). Expansions in primary care availability and health insurance coverage were associated with a reduction of about $1.6 million in PH burden at the highest level of expansion. Conclusions. Availability of primary care resources and health insurance coverage in hospitalized non-elderly adults in Harris County are significantly associated with the risk and costs of PHs. Expansions in these primary care access factors can be expected to produce significant reductions in the burden of PHs in Harris County.^

Social integration, health behaviors and health status among Mexican-American women in the *southwestern United States

Using the Hispanic Health and Nutrition Examination Survey (HHANES), this research examined several health behaviors and the health status of Mexican American women. This study focused on determining the relative impact of social contextual factors: age, socioeconomic status, quality of life indicators, and urban/rural residence on (a) health behaviors (smoking, obesity and alcohol use) and (b) health status (physician's assessment of health status, subject's assessment of health status and blood pressure levels). In addition, social integration was analyzed. The social integration indicators relate to an individual's degree of integration within his/her social group: marital status, level of acculturation (a continuum of traditional Mexican ways to dominant U.S. cultural ways), status congruency, and employment status. Lastly, the social contextual factors and social integration indicators were examined to identify those factors that contribute most to understanding health behaviors and health status among Mexican American women.^ The study found that the social contextual factors and social integration indicators proved to be important concepts in understanding the health behaviors. Social integration, however, did not predict health status except in the case of the subject's assessment of health status. Age and obesity were the strongest predictors of blood pressure. The social contextual factors and obesity were significant predictors of the physician's assessment of health status while acculturation, education, alcohol use and obesity were significant predictors of the subject's assessment of health status. ^

The association between academic achievement and health status among eighth -grade Hispanic students in Houston

This cross-sectional study examines the association between health and academic achievement among Hispanic eighth-grade students in the Houston Independent School District. As part of the district's 3 year Safe Schools/Healthy Students Initiative to enhance comprehensive educational programs, a brief anonymous questionnaire was administered in the classroom to 359 students in two schools during a one-month period in the early part of the 2001 school year. ^ The primary study questions are: Among this sample of Hispanic adolescents, is there a significant association between academic achievement and health status? and in this same population, is there a significant association between health risk behavior and health status? The specific aims of this research are: (1) to describe the association between academic achievement and health status; (2) to describe the association between health risk behaviors and health status; and (3) to describe the relative contribution of health risk behaviors and academic achievement to adolescent health status among this sample of Hispanic adolescents. ^ The survey instrument was a 32-item questionnaire that incorporated: several academic achievement questions measuring usual grades, school-related performance, attendance, student and perceived parental satisfaction with academic achievement, and educational aspirations; two health and quality of life scales measuring adolescent self-reported health; and specific measures of health risk behavior, e.g., frequency of tobacco cigarette smoking, alcohol and other drug use, aggression, and suicidal ideation and behavior that were incorporated from the national Youth Risk Behavior Survey. Questions pertaining to sexual behavior and pregnancy were omitted to comply with school district guidelines. ^ Analysis revealed that strong associations between academic achievement and health status and between health risk behaviors and health status were observed after controlling for the covariates. Eight factors were found to be significantly associated with poor health status: usual grades (low), academic performance (low), academic achievement beliefs (low), classroom and homework performance satisfaction (low), ever drinking alcohol (6 or more times), suicidality (ever thought about, planned for, or sought medical help after attempting suicide), gender (female), and age (15 years and older). (Abstract shortened by UMI.) ^

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) and Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic (2004-2012)

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2007

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2005

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2008

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic in May 2005

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2006

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic in May 2006

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.