Aim 1: Determine whether the receipt of aggressive care in the last 30 days of life among hospitalized older adults with cancer is associated with hospital Magnet recognition. Hypothesis: Patients in Magnet hospitals will be less likely to receive aggressive care, including chemotherapy in the last 14 days of life; more than 1 emergency room visit in the last 30 days of life; more than 1 hospitalization in the last 30 days of life; 1 or more ICU admissions in the last 30 days of life; in-hospital death; not admitted to hospice, or admitted to hospice for less than 3 days. Aim 2: Determine whether Black patients in Magnet hospitals receive less aggressive care in the last 30 days of life compared with Black patients in non-Magnet hospitals. Hypothesis: Black patients will be less likely than Whites to be cared for in Magnet hospitals. Black patients in Magnet hospitals will be less likely to receive aggressive care compared with Black patients in non-Magnet hospitals. Despite widespread efforts to improve patient-centered end of life care, older adults with cancer often receive poor quality care characterized by aggressive medical intervention which is often in conflict with their preferences for maximizing quality of life. We build on previous research, to generate new knowledge about the role of hospital nurses’ work environments in the provision of patient and family-centered care for terminally ill patients. If our hypotheses are confirmed, the findings from our study will inform efforts to improve care for seriously ill older adults who are hospitalized at the end of life.
(1) To design and pilot a mobile-phone-based high-frequency survey platform to measure sexual behavior and contraceptive use among young women. The app will be used to gather weekly data on sexual behavior and contraception use in a highly anonymized, private forum. By developing an appealing, user-friendly mobile app, we will both increase participant adherence and the accuracy of this sensitive data. We will also be able to gather data at a much higher frequency than is practical with in-person surveys, allowing the measurement of how contraception use varies across partners and encounters, which will reveal which barriers are materially important to use. (2) To launch this survey to 1,000 female undergraduates at the University of Zambia and collect weekly data for 12 months, beginning in fall 2018. This population is important and under-studied: they are women with a high demonstrated value of education, and thus high opportunity cost of pregnancy. Nonetheless, focus groups suggest barriers to contraception use still persist in this group, but little is known about the actual incidence of unintended pregnancy, or it’s consequences. This weekly survey, consisting of 5-10 questions about last sexual encounter, including method of contraceptive (for users), and barriers to usage (for non-users), pregnancy if it occurs, and subsequent outcomes, will allow us to generate high frequency data on women’s contraceptive choices and outcomes. We will recruit women in their dorms using female surveyors at the beginning of the term. Each week, women will be prompted by the application to fill out the survey, and their responses will be sent to the researchers on a secure server. (3) To analyze these data to better understand the extent to which pregnancy can be a barrier to tertiary education among this population, and to document potential barriers to contraceptive use. The unique, high frequency data on sexual encounters, pregnancy, and contraceptive use across different encounters collected through the app will provide several new insights, providing evidence that could be used in the optimal design of policy and contraception-promoting interventions. Multiple observations across encounters and partners for a given woman will help disentangle the quantitative importance of different barriers to contraceptive use, including those that are woman-specific (which require access or informational interventions, partner-specific (and could be addressed by bargaining interventions, or example), or encounter-specific (which might be addressed by greater access to long-acting methods). The longitudinal nature of the data will allow us to document the incidence of pregnancy in this important population, as well as the frequency of unintended pregnancy. Moreover, we will use the fine timing of the data to document the extent to which pregnancy could be causing dropout, as well as other outcomes following pregnancy.
Conduct a pilot randomized controlled trial with ~100 men engaged in fishing and other income-generating activities and offer 50 of them 3 months of lottery-based incentives to save money using mobile phone-based savings accounts. We will develop the prize-linked savings intervention through consultation with Kenya’s leading ‘mobile money’ service provider. We will also develop education and counseling materials to accompany the intervention. Over a 3-month follow-up period, we will compare financial savings and expenditures on key items such as food and items of interest from the perspective of HIV risk (alcohol, gifts and transfers to sexual partners, and sexual behavior) between those offered the prize-linked savings intervention and the control group. We will primarily use survey methods to measure the main outcomes and assess whether the intervention has adverse consequences. In summary, the pilot project will assess the feasibility and acceptability of an innovative prize-linked savings intervention. It will also generate vital preliminary data on likely effect sizes and develop recruitment procedures and data collection tools – all of which will be useful for an R01 proposal to conduct a larger randomized trial that will rigorously test the effect intervention on health and economic outcomes.
Aim 1: Empirically quantify the impact of automatic enrollment retirement policies on retirement savings and total savings. Aim 2: Test the hypothesis that the perceived costs of retirement saving prevent workers from saving for retirement. Aim 3: Estimate the size of the upfront costs. Aim 4: Explore alternative optimal default policies