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Racial and ethnic minorities utilize less healthcare than their similarly situated white counterparts in the United States, resulting in speculation that these actions may stem in part from less desire for care. In order to adequately understand the role of care-seeking for racial and ethnic disparities in healthcare, we must fully and systematically consider the complex set of social factors that influence healthcare seeking and use.

Data for this study come from a 2005 national survey of community-dwelling Medicare beneficiaries (N = 2,138). We examine racial and ethnic variation in intentions to seek care, grounding our analyses in the behavioral model of healthcare utilization. Our analysis consists of a series of nested multivariate logistic regression models that follow the sequencing of the behavioral model while including additional social factors.

We find that Latino, Black, and Native American older adults express greater preferences for seeking healthcare compared to whites. Worrying about one’s health, having skepticism toward doctors in general, and living in a small city rather than a Metropolitan Area, but not health need, socioeconomic status, or healthcare system characteristics, explain some of the racial and ethnic variation in care-seeking preferences. Overall, we show that even after comprehensively accounting for factors known to influence disparities in utilization, elderly racial and ethnic minorities express greater desire to seek care than whites.

We suggest that future research examine social factors such as unmeasured wealth differences, cultural frameworks, and role identities in healthcare interactions in order to understand differences in care-seeking and, importantly, the relationship between care-seeking and disparities in utilization.

This study represents a systematic analysis of the ways individual, social, and structural context may account for racial and ethnic differences in seeking medical care. We build on healthcare seeking literature by including more comprehensive measures of social relationships, healthcare and system-level characteristics, and exploring a wide variety of health beliefs and expectations. Further, our study investigates care seeking among multiple understudied racial and ethnic groups. We find that racial and ethnic minorities are more likely to say they would seek healthcare than whites, suggesting that guidelines promoting the elicitation and understanding of patient preferences in the context of the clinical interaction is an important step toward reducing utilization disparities. These findings also underscore the notion that health policy should go further to address the broader social factors relating to care-seeking in the first place.

The objectives of the current interim report are to measure the extent of the access to care problem, identify and compare the types of patient- and system-based barriers experienced by Vietnam veterans at risk for suicide when seeking care for physical, psychiatric, and substance abuse conditions, analyze patient-perceived quality of care for individuals who obtained access to care, and identify how the care-seeking experience effected future care seeking. This study is based on a longitudinal sample of 494 Vietnam veterans discharged from military service in September 1971 and subsequently identified as at risk for suicide (306 low risk; 188 high risk). Seventy-one percent (350) of 494 participants completed an extensive qualitative and quantitative interview covering, among other topics, physical conditions, psychiatric conditions, substance use, barriers to care, facilitators of care, and quality of care. Barriers, satisfaction, and effect of the experience were compared by type of condition and suicidal risk category using χ2 analysis and Fisher's as appropriate. The analysis is based on 257 interviews (73 percent) with qualitative data transcribed thus far. Results: Of the 195 patients with self-reported health conditions, 76 (39.0 percent) and 45 (23.1 percent) expressed system-based barriers to care, respectively. The group at higher risk of suicide was significantly more likely (p<0.01) to report patient-based barriers to care and system-based barriers to care (p<0.05), and more likely (p<0.05) to experience negative effects of the care-seeking experience. Both self-perceived and system-based barriers to care pose obstacles for patients at high risk of suicide. Targeted interventions are required to reach out to these patients to address needs for care currently unmet by the health care system and to reduce negative effects of the health care experience.

Objective – Nepal's Safe Delivery Incentive Programme (SDIP) was introduced nationwide in 2005 with the aim of encouraging greater use of professional care at childbirth. It provided cash to women giving birth in a public health facility and an incentive to the health provider for each delivery attended, either at home or in the facility. We aimed to assess the impact of the programme on neonatal mortality and health care seeking behaviour at childbirth in one district of Nepal.

Methods – Impacts were identified using an interrupted time series approach, applied to household data. We estimated a model linking the level of each outcome at a point in time to the start of the programme, demographic controls, a vector of time variables and community-level fixed effects.

Findings – The recipients of the cash transfer in the programme's first two years were disproportionately wealthier households, reflecting existing inequality in the use of government maternity services. In places with women's groups – where information about the policy was widely disseminated – the SDIP substantially increased skilled birth attendance, but failed to impact on either neonatal mortality or the caesarean section rate. In places with no women's groups, the SDIP had no impact on utilisation outcomes or neonatal mortality.

Implications for policy – The lack of any impact on neonatal mortality suggests that greater increases in utilisation or better quality of care are needed to improve health outcomes. The SDIP changed health care seeking behaviour only in those areas with women's groups highlighting the importance of effective communication of the policy to the wider public.

We present findings from a National Institute on Drug Abuse funded study of drug use and health care. Our data indicate that stigmatization of illicit drug-users in health care settings and health care providers' diagnostic focus on the contributions of drug use to their illnesses delays treatment, lengthens hospitalizations, and increases health care costs. Our findings show that, not unlike their conventional counterparts, drug users consult their peers for advice, self-treatment, referrals to services, or simply to obtain non-judgmental counsel prior to or in lieu of seeking formal care. Such lay consultation processes influence symptom recognition, attribution, and management as well as the process of formal care seeking and its timing. Discussions with drug-using peers shape perceptions of potential problematic health care interactions. In this regard, interviewees and their lay consultants believe that to be labeled as a drug user, either through self-admission or medical diagnosis, compromises the care they receive. We conclude our chapter by discussing appropriate strategies to improve the quality of care and lower the health care costs of treating drug-using patients. Such interventions include acknowledging and supporting existing lay consultation processes, disseminating better health care information through and within drug users' networks, and encouraging health care providers to conduct more holistic evaluations of drug users' health and illnesses.

Sterilisation in India (and globally) has a contentious and deeply politicised history. Despite this troubling legacy, India continues to rely on female sterilisation as the main form of contraception and family planning. Abortion, which has been legal under broad grounds since 1971, intersects with sterilisation at different points over women's reproductive lifecourse. Drawing on three case studies exploring women's abortion trajectories in Karnataka, India (2017), this chapter examines sterilisation as a reproductive technology (RT) in women's abortion narratives. These include experiences of failed sterilisation necessitating abortion, as well as narratives around pre- and post-abortion counselling with sterilisation conditionalities. Women report healthcare workers shaming or scolding them for not being sterilised after their last pregnancy – demonstrating the prominence of sterilisation as an enforced social norm using ‘health’ frames. Using reproductive justice (RJ) as a lens, I analyse how sterilisation interacts with abortion and the narratives of shame and stigma that surround the two technologies and make visible the ways in which it results in the denial and restriction of women's reproductive freedoms.

This chapter provides both an introduction to the volume and a brief review of literature on women, gender, and health and health-care services as well as racial/ethnic minorities in the same areas.

The chapter argues for the importance of greater examination of women, issues of gender, and racial and ethnic minorities in health and health-care services.

The chapter reviews the issues of women and racial and ethnic minorities and previews this book.

U.S. health policy promotes HIV testing and linkage to care (test-and-treat) with an emphasis on high risk groups such as convicted offenders. We sought to identify whether or not laws for mandatory HIV disclosure to sexual partners are a barrier to HIV testing among offenders under community supervision.

A total of 197 probationers and parolees were surveyed in a closed/item-open-ended item methodology on two reporting days in Alabama. Three main questions were asked: (1) What do offenders know about HIV? (2) What do they know about the law? (3) Do they support mandatory disclosure and HIV testing? Data for the quantitative items were analyzed with SPSS and matched with open-ended responses for explanatory purposes.

Testing and criminalization of non-disclosure were fully supported as key elements of HIV prevention. This support was framed by conceptions of HIV as a killer disease, of people with HIV as potential murderers, and by low self-awareness of HIV risk.

While the study involved only a single group of convicted offenders in a southern state, the results suggest that disclosure laws legitimize HIV stigma and undermine test-and-treat strategies among communities at risk.

The research is the first of its kind to investigate possible links between HIV criminalization and barriers to HIV prevention and care among convicted offenders.

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.