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Purpose – One-third of the world's population is infected with tuberculosis (TB) and there are two million TB-related deaths worldwide every year. Along the U.S.-Mexico border, migration patterns, and reduced access to health care contribute to high rates of TB. Delayed diagnosis of TB, the focus of this chapter, increases the likelihood that a patient will progress to more advanced stages of the disease and heightens the risk of TB transmission to others as patients are contagious for longer periods of time.

Approach – Despite the seriousness of these consequences, few sociological studies have examined delayed diagnosis of TB and why people affected by TB symptoms delay care. Because of this, we take a health narratives approach to understanding the experiences of 15 TB patients of Mexican descent in a high-risk border community (e.g., El Paso, Texas) in order to discover why delayed diagnoses happen and how they impact patients.

Findings – Fourteen of the fifteen patients experienced delayed diagnosis. Analysis of these fourteen narratives revealed two broad themes: (1) provider lack of awareness, including repeated misdiagnosis and TB test errors, and (2) patient disadvantage, including fear of U.S. immigration authorities and few economic resources for care.

Implications – Findings from this study suggest that prompt diagnosis of TB could be achieved if providers were more cognizant of TB and its symptoms and public health policies increased access to health care regardless of immigration status or socioeconomic status.

Healthcare innovations for bottom-of-pyramid populations face considerable risks and few economic incentives. Can entrepreneurial innovators provide new solutions for global health? This chapter examines how a technology enterprise built a collaborative network and supportive ecosystem making it possible to steer an innovation for TB patients through discovery, development, and delivery. Ecosystem resources were mobilized and upstream and downstream co-innovation risks were mitigated to commercialize a new diagnostic test. Detailed evidence on this innovation for TB care uses ecosystem analysis to clarify core issues in the context of joint value creation. The case study shows how resources from private and public partners can be leveraged and combined by the focal firm to build joint value and to lower execution, co-innovation, and adoption risks in healthcare ecosystems combating diseases of poverty.

The ideal of an open, all-inclusive, and participatory internet has been undermined by the rise of gender-based and misogynistic abuse on social media platforms. Limited progress has been made at supranational and national levels in addressing this issue, and where steps have been taken to combat online violence against women (OVAW), they are typically limited to legislative developments addressing image-based sexual abuse. As such, harms associated with image-based abuse have gained recognition in law while harms caused by text-based abuse (TBA) have not been conceptualized in an equivalent manner.

This chapter critically outlines the lack of judicial consideration given to online harms in British courts, identifying a range of harms arising from TBAs which currently are not recognized by the criminal justice system. We refer to non-traditional harms recognized in cases heard before the British courts, assessing these in light of traditionally recognized harms in established legal authorities. This chapter emphasizes the connection between the harms suffered and the recognition of impact on the victims, demonstrated through specific case studies. Through this assessment, this chapter advocates for greater recognition of online harms within the legal system – especially those which take the forms of misogynistic and/or gendered TBA.

In this chapter we provide analytical and Monte Carlo evidence that Chow and Predictive tests can be consistent against alternatives that allow structural change to occur at either end of the sample. Attention is restricted to linear regression models that may have a break in the intercept. The results are based on a novel reparameterization of the actual and potential break point locations. Standard methods parameterize both of these locations as fixed fractions of the sample size. We parameterize these locations as more general integer-valued functions. Power at the ends of the sample is evaluated by letting both locations, as a percentage of the sample size, converge to 0 or 1. We find that for a potential break point function, the tests are consistent against alternatives that converge to 0 or 1 at sufficiently slow rates and are inconsistent against alternatives that converge sufficiently quickly. Monte Carlo evidence supports the theory though large samples are sometimes needed for reasonable power.

Spending constraints in state budgets have resulted in a need to evaluate the effects of alternative budgeting techniques. We study public school administrations, where improvements in budgetary processes could help align system goals with reduced levels of funding. A budgeting technique, called strategic budgeting (SB), emphasizing information symmetry and mutual monitoring, is investigated in a nonprofit setting by comparing it to a traditional budgeting (TB) method. The experiment finds that the effect of reduced spending previously discovered in a corporate setting are also evident in a not-for-profit setting. Results indicated an overall cost savings with SB of almost 25 percent. Public school administrators made spending decisions in a hypothetical three-year task and provided comments to justify their decisions. These comments along with anecdotal evidence from prior field research indicate that collaborative characteristics in a budget format may reduce unnecessary spending.

We analyze Lagrange Multiplier (LM) tests for a shift in trend of a univariate time series at an unknown date. We focus on the class of LM statistics based on nonparametric kernel estimates of the long run variance. Extending earlier work for models with nontrending data, we develop a fixed-b asymptotic theory for the statistics. The fixed-b theory suggests that, for a given statistic, kernel, and significance level, there usually exists a bandwidth such that the fixed-b asymptotic critical value is the same for both I(0) and I(1) errors. These “robust” bandwidths are calculated using simulation methods for a selection of well-known kernels. We find when the robust bandwidth is used, the supremum statistic configured with either the Bartlett or Daniell kernel gives LM tests with good power. When testing for a slope change, we obtain the surprising finding that less trimming of potential shift dates leads to higher power, which contrasts the usual relationship between trimming and power. Finite sample simulations indicate that the robust LM statistics have stable size with good power.