Learning from low income countries: experience in low income countries should count towards specialist registrar training.
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Developing Countries↗
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Cash streams: five powerful income streams to increase your net income.
You can dramatically increase your profits by: Cash stream #1--extending credit and earning interest on the unpaid balance; Cash stream #2--doing all of the undone treatment in your practice; Cash stream #3--providing financing for everyone who deserves it; Cash stream #4--treating bigger cases; Cash stream #5--avoid treating deadbeats. There isn't anything I know of, which will jump start your practice as much as these five cash streams--more new patients, better case acceptance as well as increased cash flow. But you must get good at financing. You must have in place an organized, proven, financing system--just like the finance companies do.
Patient Acceptance of Health Care↗
Income inequality and household income are both important for maternal health
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Journal Article↗
Heat or eat: the Low Income Home Energy Assistance Program and nutritional and health risks among children less than 3 years of age.
OBJECTIVES: Public funding for the Low Income Home Energy Assistance Program has never been sufficient to serve more than a small minority of income-eligible households. Low Income Home Energy Assistance Program funding has not increased with recent rapidly rising energy costs, harsh winter conditions, or higher child poverty rates. Although a national performance goal for the Low Income Home Energy Assistance Program is to increase the percentage of recipient households having > or = 1 member < or = 5 years of age, the association of income-eligible households' receipt of the Low Income Home Energy Assistance Program with indicators of well-being in young children has not been evaluated previously. The goal of the current study was to evaluate the association between a family's participation or nonparticipation in the Low Income Home Energy Assistance Program and the anthropometric status and health of their young children. METHODS: In the ongoing Children's Sentinel Nutrition Assessment Project from June 1998 through December 2004, caregivers with children < 3 years of age in 2 emergency departments and 3 primary care clinics in 5 urban sites participated in cross-sectional surveys regarding household demographics, child's lifetime history of hospitalizations, and, for the past 12 months, household public assistance program participation and household food insecurity, measured by the US Food Security Scale. This scale, in accordance with established procedures, classifies households as food insecure if they report that they cannot afford enough nutritious food for all of the members to lead active, healthy lives. On the day of the interview, children's weight, length, and whether the children were admitted acutely to the hospital from the emergency departments were documented. The study sample consisted only of Low Income Home Energy Assistance Program income-eligible renter households without private insurance who also participated in > or = 1 other means-tested program. RESULTS: In this sample of 7074 caregivers, 16% of families received the Low Income Home Energy Assistance Program, similar to the national rate of 17%. Caregivers who received the Low Income Home Energy Assistance Program were more likely to be single (63% vs 54%), US born (77% vs 68%), and older (mother's mean age: 28.1 vs 26.7 years) but were less likely to be employed (44% vs 47%). Households who received the Low Income Home Energy Assistance Program were more likely to receive Supplemental Nutrition Program for Women, Infants, and Children (85% vs 80%), Supplemental Security Income (13% vs 9%), Temporary Assistance for Needy Families (38% vs 23%), and food stamps (59% vs 37%) and to live in subsidized housing (38% vs 19%) compared with nonrecipients. Children in families participating in the Low Income Home Energy Assistance Program were older than children in nonparticipating families (13.6 vs 12.5 months), were less likely to be uninsured (5% vs 9%), and were more likely to have had a low birth weight < or = 2500 g (17% vs 14%). Families participating in the Low Income Home Energy Assistance Program reported more household food insecurity (24% vs 20%) There were no significant group differences between recipients and nonrecipients in caregiver's education or child's gender. After controlling for these potentially confounding variables, including receipt of other means-tested programs, compared with children in recipient households, those in nonrecipient households had greater adjusted odds of being at aggregate nutritional risk for growth problems, defined as children with weight-for-age below the 5th percentile or weight-for-height below the 10th percentile, with significantly lower mean weight-for-age z scores calculated from age- and gender-specific values from the Centers for Disease Control and Prevention 2000 reference data. However, in adjusted analyses, children aged 2 to 3 years in recipient households were not more likely to be overweight (BMI > 95th percentile) than those in nonrecipient households. Rates of age-adjusted lifetime hospitalization excluding birth and the day of the interview did not differ between Low Income Home Energy Assistance Program recipient groups. Among the 4445 of 7074 children evaluated in the 2 emergency departments, children from eligible households not receiving the Low Income Home Energy Assistance Program had greater adjusted odds than those in recipient households of acute hospital admission on the day of the interview. CONCLUSIONS: Even within a low-income renter sample, Low Income Home Energy Assistance Program benefits seem to reach families at the highest social and medical risk with more food insecurity and higher rates of low birth-weight children. Nevertheless, after adjustment for differences in background risk, living in a household receiving the Low Income Home Energy Assistance Program is associated with less anthropometric evidence of undernutrition, no evidence of increased overweight, and lower odds of acute hospitalization from an emergency department visit among young children in low-income renter households compared with children in comparable households not receiving the Low Income Home Energy Assistance Program. The Low Income Home Energy Assistance Program in many states shuts down early each winter when their funding is exhausted. From a clinical perspective, pediatric health providers caring for children from impoverished families should consider encouraging families of these children to apply for the Low Income Home Energy Assistance Program early in the season before funding is depleted. From a public policy perspective, although this cross-sectional study design can only demonstrate associations and not causation, these findings suggest that, particularly as fuel costs and children's poverty rates increase, expanding the Low Income Home Energy Assistance Program funding and meeting the national Low Income Home Energy Assistance Program performance goal of increasing the percentage of recipient households with young children might potentially benefit such children's growth and health.
Child, Preschool↗
Income and Mental Health: Unraveling Community and Individual Level Relationships.
BACKGROUND: The association between individual socioeconomic status (SES) and mental disorder is well-documented, but studies to date have provided limited and sometimes conflicting evidence on the relationship between aspects of socioeconomic environment, including the role of income inequality, and mental disorder. AIMS OF THE STUDY: This paper explores the relationships between mental disorder and individual SES and socioeconomic environment, with particular attention to both the level and dispersion of community income and to their interactions with individual income. METHODS: Cross sectional study using nationally representative, individual level data from the Healthcare for Communities survey merged with supplemental information. Dependent variable is individual mental health status, measured by the 5 item Mental Health Inventory (MHI-5; average 80.6) and an indicator of probable anxiety or mood disorder based on clinical screening instruments (positive for 14.3 percent of respondents in the sample). RESULTS: MHI-5 decreases (indicating worse mental health), and the probability of an anxiety or depressive disorder increases continuously from the highest to the lowest quintiles of family income. Compared to those in the highest income quintile, MHI-5 is more than 10 points lower and the probability of disorder is much greater among individuals in the lowest income quintile. Within-quintile own income level is also strongly associated with mental health among lower income individuals. We find no evidence that higher levels of income inequality are associated with poor mental health outcomes, measured either by the probability of disorder or MHI-5. Regarding income level, MHI-5 is 3.4 to 3.5 points higher among low income individuals in medium or high income states compared to those in low income states. DISCUSSION: The qualitative conclusions are stable across various specifications reported (two different measures of mental health, two geographic levels, and among all individuals and low income individuals alone), and in specifications with alternative parameterizations of the community variables (continuously measured, included as quintiles instead of tertiles, and using other indicators of inequality). Individual income is highly correlated with mental health status; level of state income has some association; community or state income inequality has no detectable relationship with mental health. This analysis provides no support for the hypothesis that income inequality is a stronger determinant of health than individual or family income, a hypothesis that in recent years has received much attention in the popular press and policy debates. Limitations of our analysis include the cross-sectional nature of the analysis, the sample sizes used in derivation of the site variables (though sensitivity analyses showed robust results) and the age of the state data. CONCLUSIONS: The association between individual income and mental health is strong. No support for the income inequality hypothesis is found.IMPLICATIONS FOR HEALTH POLICY FORMULATION: Our findings point to a need for better understanding of the relationship between individual income and mental health outcomes. Our research does not support the notion that policies aimed at diminishing income inequality are an important lever in improving mental health outcomes for individuals. IMPLICATIONS FOR FURTHER RESEARCH: This research does not address whether and how different sources of income-at the individual or community level-may affect mental health, and whether the associations observed cross-sectionally also bear out longitudinally. In addition, more research into the relationship between other community characteristics, such as service availability, and mental health outcomes is needed.
Journal Article↗
Income non-reporting: implications for health inequalities research.
OBJECTIVES: To determine whether, in the context of a face to face interview, socioeconomic groups differ in their propensity to provide details about the amount of their personal income, and to discuss the likely consequences of any differences for studies that use income based measures of socioeconomic position. DESIGN AND SETTING: The study used data from the 1995 Australian Health Survey. The sample was selected using a stratified multi-stage area design that covered urban and rural areas across all States and Territories and included non-institutionalised residents of private and non-private dwellings. The response rate was 91.5% for selected dwellings and 97.0% for persons within dwellings. Data were collected using face to face interviews. Income response, the dependent measure, was binary coded (0 if income was reported and 1 for refusals, "don't knows" and insufficient information). Socioeconomic position was measured using employment status, occupation, education and main income source. The socioeconomic characteristics of income non-reporters were initially examined using sex specific age adjusted proportions with 95% confidence intervals. Multivariate analysis was performed using logistic regression. PARTICIPANTS: Persons aged 15-64 (n = 33,434) who were reportedly in receipt of an income from one or more sources during the data collection reference period. RESULTS: The overall rate of income non-response was 9.8%. Propensity to not report income increased with age (15-29 years 5.8%, 30-49 10.6%, 50-64 13.8%). No gender differences were found (men 10.2%, women 9.3%). Income non-response was not strongly nor consistently related to education or occupation for men, although there was a suggested association among these variables for women, with highly educated women and those in professional occupations being less likely to report their income. Strong associations were evident between income non-response, labour force status and main income source. Rates were highest among the employed and those in receipt of an income from their own business or partnership, and lowest among the unemployed and those in receipt of a government pension or benefit (which excluded the unemployed). CONCLUSION: Given that differences in income non-reporting were small to moderate across levels of the education and occupation variables, and that propensity to not report income was greater among higher socioeconomic groups, estimates of the relation between income and health are unlikely to be affected by socioeconomic variability in income non-response. Probability estimates from a logistic regression suggested that higher rates of income non-reporting among employed persons who received their income from a business or partnership were not attributable to socio-economic factors. Rather, it is proposed that these higher rates were attributable to recall effects, or concerns about having one's income information disclosed to taxation authorities. Future studies need to replicate this analysis to determine whether the results can be inferred to other survey and data collection contexts. The analysis should also be extended to include an examination of the relation between socio-economic position and accuracy of income reporting. Little is known about this issue, yet it represents a potential source of bias that may have important implications for studies that investigate the association between income and health.
Adolescent↗
Letting the Gini out of the bottle? Challenges facing the relative income hypothesis.
The relative income hypothesis interprets statistical associations between income inequality and average health status at the population level, as evidence that income inequality has a deleterious psychosocial effect on individual health. An alternative explanation is that these, population-level associations, are statistical artefacts of curvilinear, individual-level relationships between income and health. Indeed, provided the cost-benefit ratio of health-enhancing goods and services vary, the law of diminishing returns should produce curvilinear, asymptotic relationships between income and health at the individual level, which create ('artefactual') associations between income inequality and health at the population level. However, proponents of the relative income hypothesis have argued that these relationships are unlikely to be responsible for the associations observed between income inequality and average health status amongst high-income populations. In these populations, the individual-level relationships between income and health would be nearer their asymptotes, where a shallower slope should ensure that income inequality has little (if any) 'artefactual' effect on average health status. Yet this argument was based on analyses of population-level data which underestimated the slope and curvilinearity of underlying, individual-level relationships between income and health. It is therefore likely that (at least some part of) the population-level associations between income inequality and average health status (amongst low-, middle- and high-income populations) are 'artefacts' of curvilinear, individual-level relationships between income and health. Nevertheless, it is also possible that income inequality is somehow (partly or wholly) responsible for the curvilinear nature of individual-level relationships between income and health. Likewise, it is possible that income inequality alters the height, slope and/or curvilinearity of these relationships in such a way that income inequality has an independent effect on individual health. In either instance, the 'artefactual' effect of curvilinear relationships between income and health at the individual level would simply reflect the mechanism underlying the relative income hypothesis.
Female↗
Health care for children and youth in the United States: annual report on patterns of coverage, utilization, quality, and expenditures by income.
OBJECTIVES: To examine differences by income in insurance coverage, health care utilization, expenditures, and quality of care for children in the United States. METHODS: Two national health care databases serve as the sources of data for this report: the 2000-2002 Medical Expenditure Panel Survey (MEPS) and the 2001 Nationwide Inpatient Sample (NIS) from the Healthcare Cost and Utilization Project (HCUP). In the MEPS analyses, low income is defined as less than 200% of the federal poverty level and higher income is defined as 200% of the federal poverty level or more. For the HCUP analyses, median household income for the patient's zip code of residence is used to assign community-level income to individual hospitalizations. RESULTS: Coverage. Children from low-income families were more likely than children from middle-high-income families to be uninsured (13.0% vs 5.8%) or covered by public insurance (50.8% vs 7.3%), and less likely to be privately insured (36.2% vs 87.0%). Utilization. Children from low-income families were less likely to have had a medical office visit or a dental visit than children from middle-high-income families (63.7% vs 76.5% for office-based visits and 28.8% vs 51.4% for dental visits) and less likely to have medicines prescribed (45.1% vs 56.4%) or have utilized hospital outpatient services (5.2% vs 7.0%), but more likely to have made trips to the emergency department (14.6% vs 11.4%). Although low-income children comprise almost 40% of the child population, one quarter of total medical expenditures were for these children. Hospital Discharges. Significant differences by community-level income occurred in specific characteristics of hospitalizations, including admissions through the emergency department, expected payer, mean total charges per day, and reasons for hospital admission. Leading reasons for admission varied by income within and across age groups. Quality. Low-income children were more likely than middle-high-income children to have their parents report a big problem getting necessary care (2.4% vs 1.0%) and getting a referral to a specialist (11.5% vs 5.3%). Low-income children were at least twice as likely as middle-high-income children to have their parents report that health providers never/sometimes listened carefully to them (10.0% vs 5.1%), explained things clearly to the parents (9.6% vs 3.4%), and showed respect for what the parents had to say (9.2% vs 4.2%). Children from families with lower community-level incomes were more likely to experience ambulatory-sensitive hospitalizations. Racial/Ethnic Differences Between Income Groups. Use and expenditure patterns for most services were not significantly different between low- and middle-high-income black children and were lower than those for white children. CONCLUSIONS: While health insurance coverage is still an important factor in obtaining health care, the data suggest that efforts beyond coverage may be needed to improve access and quality for low-income children overall and for children who are racial and ethnic minorities, regardless of income.
Adolescent↗
Family income and the impact of a children's health insurance program on reported need for health services and unmet health need.
OBJECTIVE: In an era when expanding publicly funded health insurance to children in higher income families has been the major strategy to increase access to health care for children, it is important to determine if the benefits to higher income children attributable to the receipt of health coverage are similar to those observed for lower income children. This study investigated how the likely impact of child health insurance expansions varies with family income. METHODS: We surveyed parents or guardians of children who were enrolled in a state-sponsored health insurance program (Massachusetts Children's Medical Security Plan [CMSP]) that, before the implementation of the State Children's Health Insurance Plan (SCHIP), was open to all children regardless of income. A stratified sample of children was drawn from administrative files. We grouped children by income category (low-income [LI]: < or =133% of the federal poverty limit [FPL], middle-income [MI]: 134%-200% of the FPL, high-income [HI]: >200% of the FPL) that corresponded to eligibility for public health insurance programs in the state (Medicaid-eligible, SCHIP-eligible, and income that exceeded SCHIP eligibility). The majority of telephone interviews were conducted between November 1998 and March 1999. The overall response rate was 61.8%, yielding a sample of 996 children. The CSMP benefit package included comprehensive coverage for preventive and specialty care and limited coverage for ancillary services. Children enrolled in CMSP were not covered for inpatient hospital stays but those whose family income was <400% of the FPL were eligible to receive full or partial coverage for inpatient care through the state's free care pool. Although the CMSP benefit package did not meet the standards for a SCHIP, it is an approximate equivalent for children with incomes <200% of the FPL, who have full coverage for hospitalization through the state's free care pool. We used survey responses to develop 2 sets of indicators: the first for reported need for services and the second for unmet need or delays in care among children whose parents reported a need for the service. Within each set, we created indicators for 5 types of service (medical care, dental care, prescription drugs, vision services, and mental health care) and an additional composite indicator. The composite indicator aggregated all categories of services covered under CMSP in a single measure; it included all services except dental services, which, at the time of the study, were not covered by the program. The composite indicator served as the dependent variable in regression models. We used weighted chi2 tests to identify statistically significant differences in reported need and unmet need for the 5 types of medical services and the aggregate measure of all services covered by CMSP. We examined differences across income groups at 2 points in time: during the period children were uninsured before enrollment and while enrolled. We used weighted logistic regression to assess the independent association of family income with our dependent variables: reported need for health services and the presence of unmet need, controlling for other covariates. To evaluate the impact of participation in a child health insurance program, we examined unmet need before and after program enrollment, testing for statistical significance using McNemar's test for within-subject changes. RESULTS: During the period of uninsurance before enrollment, prescription drugs (70%) was the health service needed most frequently, followed by medical (65%) and dental (57%) care. For the composite measure of services covered by CMSP, reported need for services was not significantly different by income. Need for medical care, dental care, and prescription drugs were significantly greater among children who had been uninsured for >6 months before enrollment. In addition, a significantly greater proportion of adolescent participants needed dental, vision, and mental health services than younger enrollees. While enrolled, among recently enrolled children, 77% need medical services, 68% prescription drugs, and 59% dental. In unadjusted models MI and HI children were more than 2 times as likely to report need for covered services as LI children. After adjusting for possible confounders, the effect of income was no longer significant. Instead, nonadolescents (odds ratio [OR]: 2.44; 95% confidence interval [CI]: 1.25-4.76) and children with white ethnicity (OR: 3.03; 95% CI: 1.43-6.67) were significantly more likely to report need for services. Before enrollment, unmet need among those who reported need for services was 5% for medical, 4% prescription drugs, 31% dental, 30% vision, and 33% mental health. For the composite measure of services covered by CMSP, LI children were significantly more likely to have had unmet need before enrollment than MI and HI children (20%, 10%, 7% by income). As compared with younger children, adolescents also had significantly greater unmet need for the composite measure (19% vs 10%). In multivariate models, not having a usual site of care was a highly significant predictor of unmet need or delayed care (OR: 3.41; 95% CI: 1.28-9.11). Ninety-eight percent of parents cited cost as the reason they had difficulty obtaining needed care. After enrollment, the proportion of children who needed care and had difficulty obtaining it decreased for all categories of care. Less than 1% of enrollees reported unmet need or delays in care for medical services and 3% for prescription drugs. Children who needed vision and mental health services continued to experience difficulty obtaining these services (17% for each category of care), although they were covered as part of the benefit package. Unmet need or delays in care for dental services, which at the time of the study were not covered under CMSP, remained high (27%). We found a significant reduction in unmet need among children in all income groups and no significant differences in unmet need by income. Controlling for other covariates, adolescents (OR: 3.11; 95% CI: 1.58-6.12) and children with compromised health (OR: 3.20; 95% CI: 1.35-7.58) were more likely to have had difficulty obtaining needed services while enrolled in the program. Children in larger families (OR: 0.40; 95% CI: 0.17-0.96) and who were previously uninsured for >6 months (OR: 0.45; 95% CI: 0.22-7.58) were less likely to have difficulty obtaining care. CONCLUSION: Our findings demonstrate the positive impact of providing health insurance coverage to children regardless of income. The HI children who enrolled in the program looked similar to children with incomes that meet current SCHIP eligibility guidelines, suggesting that expansions of SCHIPs to HI children should not qualitatively change the program dynamics.
Child↗
Geographical variation of cerebrovascular disease in New York State: the correlation with income.
BACKGROUND: Income is known to be associated with cerebrovascular disease; however, little is known about the more detailed relationship between cerebrovascular disease and income. We examined the hypothesis that the geographical distribution of cerebrovascular disease in New York State may be predicted by a nonlinear model using income as a surrogate socioeconomic risk factor. RESULTS: We used spatial clustering methods to identify areas with high and low prevalence of cerebrovascular disease at the ZIP code level after smoothing rates and correcting for edge effects; geographic locations of high and low clusters of cerebrovascular disease in New York State were identified with and without income adjustment. To examine effects of income, we calculated the excess number of cases using a non-linear regression with cerebrovascular disease rates taken as the dependent variable and income and income squared taken as independent variables. The resulting regression equation was: excess rate = 32.075-1.22 x 10(-4)(income)+ 8.068x10(-10)(income2), and both income and income squared variables were significant at the 0.01 level. When income was included as a covariate in the non-linear regression, the number and size of clusters of high cerebrovascular disease prevalence decreased. Some 87 ZIP codes exceeded the critical value of the local statistic yielding a relative risk of 1.2. The majority of low cerebrovascular disease prevalence geographic clusters disappeared when the non-linear income effect was included. For linear regression, the excess rate of cerebrovascular disease falls with income; each 10,000 dollars increase in median income of each ZIP code resulted in an average reduction of 3.83 observed cases. The significant nonlinear effect indicates a lessening of this income effect with increasing income. CONCLUSION: Income is a non-linear predictor of excess cerebrovascular disease rates, with both low and high observed cerebrovascular disease rate areas associated with higher income. Income alone explains a significant amount of the geographical variance in cerebrovascular disease across New York State since both high and low clusters of cerebrovascular disease dissipate or disappear with income adjustment. Geographical modeling, including non-linear effects of income, may allow for better identification of other non-traditional risk factors.
Journal Article↗
The epidemiology of low back pain in the rest of the world. A review of surveys in low- and middle-income countries.
STUDY DESIGN: A criteria-based review of the literature. SUMMARY OF BACKGROUND DATA: The literature on the epidemiology of low back pain is accumulating, but for the most part studies are restricted to high-income countries, which comprise less than 15% of the world's population. Little is known about the epidemiology of low back pain in the rest of the world. OBJECTIVES: To address the imbalance in the literature and to review the relatively few studies on the epidemiology of low back pain in low- and middle-income countries. Rates from these studies are contrasted with rates from selected high-income countries. In reviewing the literature, a hypothesis is tested: low back pain rates are higher in low-income countries than in high-income countries, not only because hard physical labor is more prevalent in low-income countries, but also because, unlike high-income countries, hard physical labor for older workers in low-income countries often is unavoidable. METHODS: Among other sources, articles for the review come from a search of the MEDLINE bibliographic database, with "back pain" and individual low- and middle-income countries entered as key words. To avoid recall biases, findings specifically on point prevalence are reviewed. RESULTS: Within the categories of low-income and high-income countries, low back pain rates vary twofold or more. In comparisons between these categories of countries, rates on the whole are higher among the general populations of selected high-income countries than among rural low-income populations; specifically, rates are 2-4 times higher among Swedish, German, and Belgium general populations than among Nigerian, southern Chinese, Indonesian, and Filipino farmers. Within low income countries, rates are higher among urban populations than among rural populations and still higher among workers in particular worksites, referred to as "enclosed workshops." CONCLUSIONS: The disparity in low back pain rates within categories of countries, high-income and low-income, calls attention to the high proportion of studies on the epidemiology of low back pain that are methodologically questionable. Recommendations are offered to improve the methodologic quality of this type of study. Conclusions may be drawn from comparisons between studies, although, in the absence of set methodologic standards, they are tentative. The considerably lower rates among populations of low-income farmers compared with rates of the affluent populations of selected northern European countries indicate that, contrary to the hypothesis proposed here, hard physical labor itself is not necessarily related to low back pain. The higher rates in urban low-income populations as compared with rates in rural low-income populations and the sharply higher rates among workers in enclosed workshops of low-income countries suggest a disturbing trend: low back pain prevalence may be on the rise among vast numbers of workers as urbanization and rapid industrialization proceed.
Adolescent↗
Individual-level and neighborhood-level income measures: agreement and association with outcomes in a cardiac disease cohort.
BACKGROUND: Census-based measures of income often are used as proxies for individual-level income. Yet, the validity of such area-based measures relative to 'true' individual-level income has not been fully characterized. OBJECTIVES: The objectives of this study were (1) to determine whether area-based measures of household income are a suitable proxy for self-reported household income and (2) to assess whether these measures are associated with outcomes in a cardiac disease cohort. RESEARCH DESIGN: We used a prospective cohort from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) cardiac catheterization registry. SUBJECTS: A total of 4372 patients having undergone cardiac catheterization and who also completed a 1-year follow-up questionnaire on self-reported income level were studied. MEASURES: Our measurements were survival to 2.5 years after catheterization and health-related quality of life (EuroQoL). RESULTS: Agreement between the 2 income measures generally was poor (unweighted Kappa = 0.07), particularly for the low-income patients. Despite this poor agreement, both income measures were positively associated with survival and EuroQoL scores. An outcome analysis that simultaneously considered individual level income and area-based income revealed that low-income individuals have poorer survival and lower quality of life scores if they live in low income neighborhoods, but not if they live in high income neighborhoods. CONCLUSIONS: The area-based estimates of household income in these data demonstrate poor agreement with self-reported household income at the level of individual patients, particularly for low-income patients. Despite this, both income measures appear to be prognostically relevant, perhaps because individual and neighborhood income measure different constructs.
Alberta↗
National Cancer Data Base survey of breast cancer management for patients from low income zip codes.
BACKGROUND: The National Cancer Data Base (NCDB), a joint project of the American College of Surgeons Commission on Cancer and the American Cancer Society, is a cancer management and outcomes data base for health care organizations. It provides a comparative summary of patient care that is used by participating hospitals and communities for self-assessment. The most current (1995-1996) breast cancer data on patients from low income zip codes are described here. METHODS: Since 1989, eight Calls for Data have been issued, yielding a total of 191,714 reports of non-Hispanic white patients with breast cancer for the years analyzed, 1995-1996. A total of 1961 hospital cancer registries have participated in at least one of the Calls for Data. RESULTS: A diverse range of breast cancer cases was reported from a variety of geographic locations and medical care environments. There were general similarities in the treatment of patients from the different income groups; however, some differences were reported. Among patients from lower income zip codes, 60.7% were age 60 years or older, compared with 55.1% from other income zip code groups. The AJCC stage distribution was reported as less favorable for patients from low income zip codes than for other patients. The percentage of patients from low income zip codes diagnosed as Stage 0 or I was 51.2%, compared with 55.9% of patients from the other income zip codes. Of patients from lower income zip codes, 12.1% were reported to have Stage III or IV disease, compared with 10.0% of patients from other income zip codes. Patients from low income zip codes received less tissue-sparing surgery. Of patients from low income zip codes, 14.9% received partial mastectomy with or without radiation or systemic therapy, compared with 18.3% of patients from other income zip codes. The percentage of patients from low income zip codes who received a partial mastectomy with axillary lymph node dissection was 23.3% for patients from other income zip codes, the percentage was 30.5%. Conversely, 49.8% of patients from lower income zip codes received a modified radical mastectomy, compared with 40.5% of patients from other income zip codes. CONCLUSIONS: Further improvements in the early diagnosis and surgical treatment of low income patients can probably be achieved. Programmatic activities that further explain or reduce the apparent nonpreferred treatment of some low income patients should be encouraged.
Adult↗
Associations between relative income and mortality in Norway: a register-based study.
BACKGROUND: Current research on health inequalities suggests that not only an individual's absolute level of income but also his/her relative position in the income hierarchy could have health consequences. This study examines whether relative income was associated with individuals' mortality in Norway during the 1990s. METHODS: Data were formed by linkages of Norwegian administrative registers. This study analyses 1.68 million men and women (age group: 30-66 years) with disposable income (1993) in the range 60,000-210,000 Norwegian Kroner. Relative income was calculated as deviations in per cent from the median income in the surrounding residential area. The outcome variable was deaths in 1994-1999. Effects of relative income on mortality were estimated by multiple logistic regression analyses, separately in 13 narrow brackets of absolute income. Adjustments were made for sex, education, marital status, and other individual-level mortality predictors. RESULTS: Low relative income compared with the median in residential regions with populations above 20,000 inhabitants was associated with higher mortality among those with medium and lower absolute income. The excess risk increased progressively the lower the level of absolute income. Among those with higher absolute income, however, relative income was not associated with mortality. Moreover, when relative income was considered in relation to the median in small municipalities, almost no effect on mortality was observed. CONCLUSION: In Norway during the 1990s, having low relative income constituted an additional mortality risk among individuals with middle or lower absolute incomes and when relative income was calculated in relation to the average in medium-sized or larger regions.
Adult↗
Cesarean section rates and maternal and neonatal mortality in low-, medium-, and high-income countries: an ecological study.
BACKGROUND: Cesarean section rates show a wide variation among countries in the world, ranging from 0.4 to 40 percent, and a continuous rise in the trend has been observed in the past 30 years. Our aim was to explore the association of cesarean section rates of different countries with their maternal and neonatal mortality and to test the hypothesis that in low-income countries, increasing cesarean section rates were associated with reductions in both outcomes, whereas in high-income countries, such association did not exist. METHODS: We performed a cross-sectional multigroup ecological study using data from 119 countries from 1991 to 2003. These countries were classified into 3 categories: low-income (59 countries), medium-income (31 countries), and high-income (29 countries) countries according to an international classification. We assessed the ecological association between national cesarean section rates and maternal and neonatal mortality by fitting multiple linear regression models. RESULTS: Median cesarean section rates were lower in low-income than in medium- and high-income countries. Seventy-six percent of the low-income countries, 16 percent of the medium-income countries, and 3 percent of high-income countries showed cesarean section rates between 0 and 10 percent. Three percent of low-income countries, 36 percent of medium-income countries, and 31 percent of high-income countries showed cesarean section rates above 20 percent. In low-income countries, a negative and statistically significant linear correlation was observed between cesarean section rates and neonatal mortality and between cesarean section rates and maternal mortality. No association was observed in medium- and high-income countries for either neonatal mortality or maternal mortality. CONCLUSIONS: No association between cesarean section rates and maternal or neonatal mortality was shown in medium- and high-income countries. Thus, it becomes relevant for future good-quality research to assess the effect of the high figures of cesarean section rates on maternal and neonatal morbidity. For low-income countries, and on confirmation by further research, making cesarean section available for high-risk pregnancies could contribute to improve maternal and neonatal outcomes, whereas a system of care with cesarean section rates below 10 percent would be unlikely to cover their needs.
Adult↗
Income level and asthma prevalence and care patterns.
Manitoba has a universally accessible health-care system that records physician contacts and hospitalizations in such a way that they can be ascribed to individuals. We examined the prevalence of physician-diagnosed asthma, bronchitis, and airways obstruction (total respiratory morbidity [TRM]) in Winnipeg in 1988 and 1992, using place of residence to divide people into quintiles according to average family income. Physician office visits, hospitalizations, and consultation referrals were each examined. Three age groups: 0 to 14 yr, 15 to 34 yr, and > or = 35 yr were studied. The prevalence of TRM was greater in low- than in high-income quintiles. Asthma prevalence was unrelated to income in the younger age groups; in the older group asthma was more common in low-income groups, but was less strongly related to income than was TRM. Asthma prevalence increased over the years studied, but the increase was not related to income level. There was some evidence of income-related diagnostic bias in that low-income patients were more likely to be labeled with a related diagnosis in addition to asthma than were high-income patients. Low-income patients had more physician contacts than did high-income patients. In terms of physician office visits, care continuity did not differ among income quintiles. Low-income quintiles had more hospitalizations than did high-income quintiles, and differences were larger than could be accounted for by diagnostic bias; asthma was probably more severe in low-income quintiles. High-income quintiles had more consultation referrals than did low-income quintiles.
Adolescent↗
Income and mortality: the shape of the association and confounding New Zealand Census-Mortality Study, 1981-1999.
OBJECTIVE: To determine the shape of the income-mortality association, before and after adjusting for confounding by other socioeconomic variables. METHODS: Poisson regression analyses were conducted on 11.7 million years of follow-up of 25-59 year old New Zealand census respondents spanning four separate cohort studies (1981-1984, 1986-1989, 1991-1994, and 1996-1999). RESULTS: Mortality among low-income people was approximately two times that among high-income people. Adjustment for potential socioeconomic confounders (marital status, education, car access, and neighbourhood socioeconomic deprivation) halved the strength of the income-mortality association, but did not appreciably change the shape of the association. Further adjustment for labour force status largely removed the income-mortality association. The association of non-transformed income with mortality was non-linear, with a flattening out of the slope at higher incomes. Both the logarithm and rank of income appeared to have a better linear fit with the mortality rate, although the association of mortality with the logarithm of income flattened out notably at low incomes. CONCLUSIONS: Much, but not all, of the crude association of income with mortality could be due to confounding. Adjusting income-mortality associations for labour force status (also a proxy for health status) is problematic: on the one hand, it over-adjusts the association as poor health will be on the pathway from income to mortality; on the other hand, it appropriately adjusts for both confounding by labour force status and reverse causation whereby income changes as a result of poor health. Both logarithmic and rank transformations of income have a reasonable linear fit with income.
Censuses↗
Income inequality and self rated health in Britain.
STUDY OBJECTIVE: Several studies have reported an association between income inequality and increased mortality, but few have used net income data, controlled for individual income, or evaluated sensitivity to the choice of inequality measure. The study tested the hypotheses that people in regions of Britain with the greatest income inequality would report worse health than those in other regions, after adjusting for individual socioeconomic circumstances. DESIGN: Cross sectional survey. SETTING: England, Wales, and Scotland. PARTICIPANTS: 8366 people living in private households. MAIN RESULTS: Regional income inequality, measured using the Gini index, was associated with worse self rated health, especially among those with the lowest incomes (adjusted OR 1.55, 95% CI 1.24 to 1.92) (p<0.001). This association was not robust to the choice of income inequality measure, being maximal for the Gini coefficient and weakest when using indices that are more sensitive to income differences among those at the top or bottom of the income distribution. CONCLUSIONS: The study found limited evidence of an association between income inequality and worse self rated health in Britain, which was greatest among those with the lowest individual income levels. As regions with the highest income inequality were also the most urban, these findings may be attributable to characteristics of cities rather than income inequality. The variation in this association with the choice of income inequality measure also highlights the difficulty of studying income distributions using summary measures of income inequality.
Cross-Sectional Studies↗