Building fair, equitable, and beneficial partnerships between institutions collaborating in research in low- and middle-income countries (LMIC) and high-income countries (HIC) has become an integral part of research capacity building in global health in recent years. In this paper, we offer an example of an academic collaboration between the University of California Los Angeles, Center for Health Policy and Research (UCLA CHPR) and the University of Philippines, Manila, College of Public Health (UPM CPH) that sought to build an equitable partnership between research institutions. The partnership was built on a project to build capacity for research and produce data for policy action for the prevention and care of non-communicable diseases (NCDs) through primary healthcare in the Philippines. The specific objectives of the project were to: (1) locally adapt the Primary Care Assessment Tool for the Philippines and use the adapted tool to measure facility-level primary care delivery, (2) conduct focus group discussions (FGDs) to gather qualitative observations regarding primary care readiness and capacity, and (3) conduct a comprehensive population-based health survey among adults on NCDs and prior healthcare experience. We describe here the progression of the partnership between these institutions to carry out the project and the elements that helped build a stronger connection between the institutions, such as mutual goal setting, cultural bridging, collaborative teams, and capacity building. This example, which can be used as a model depicting new directionality and opportunities for LMIC-HIC academic partnerships, was written based on the review of shared project documents, including study protocols, and written and oral communications with the project team members, including the primary investigators. The innovation of this partnership includes: LMIC-initiated project need identification, LMIC-based funding allocation, a capacity-building role of the HIC institution, and the expansion of scope through jointly offered courses on global health.

Background The number of Californians covered by Medi-Cal increased more than 50% between 2013 and 2018, largely due to expansion under the Affordable Care Act (ACA). This rapid expansion of Medicaid rolls prompted concerns that Medi-Cal enrollees would face greater difficulty accessing health care. Objective Examine whether gaps in access to care between Medi-Cal and employer-sponsored insurance (ESI) present in 2013 (prior to ACA implementation) had changed by 2018 (several years post implementation). Design Secondary analysis of data from the 2013 and 2018 California Health Interview Survey. The sample included adults of ages 18–64 insured all year and covered by ESI or Medi-Cal at time of interview. Logistic regressions were used to examine variation across years in the association between access to care and insurance type. Main Measures Five access to care outcomes were assessed: no usual source of care, not accepted as new patient in past year, insurance not accepted in past year, delayed medical care in past year, and difficulty getting timely appointment. The main predictors of interest were type of insurance (Medi-Cal or ESI) and survey year (2013 or 2018). Key Results The association between insurance type and access to care changed significantly over time for three outcomes: not accepted as new patient in past year (OR = 0.55, 95% CI = 0.32–0.97), delayed medical care in past year (OR = 1.55, 95% CI = 1.06–2.25), and difficulty getting timely appointment (OR = 0.41, 95% CI = 0.23–0.74). Predicted probabilities indicate gaps between Medi-Cal and ESI narrowed for not accepted as new patient in past year and difficulty getting timely appointment, but widened for delayed medical care. Conclusions Despite the rapid expansion in the number of Californians covered by Medi-Cal, most gaps in access to care between Medi-Cal and ESI enrollees improved or did not significantly change between 2013 and 2018.

Population-level health outcomes and measures of well-being are often described relative to broad racial/ethnic categories such as White or Caucasian; Black or African American; Latino or Hispanic; Asian American; Native Hawaiian and Pacific Islander; or American Indian and Alaska Native. However, the aggregation of data into these groups masks critical within-group differences and disparities, limiting the health and social services fields’ abilities to target their resources where most needed. While researchers and policymakers have recognized the importance of disaggregating racial/ethnic data—and many organizations have advocated for it over the years—progress has been slow and disparate. The ongoing lack of racial/ethnic data disaggregation perpetuates existing inequities in access to much-needed resources that can ensure health and well-being. In its efforts to help build a Culture of Health and promote health equity, the Robert Wood Johnson Foundation has supported activities aimed to advance the meaningful disaggregation of racial/ethnic data—at the collection, analysis, and reporting phases. This special issue presents further evidence for the importance of disaggregation, the technical and policy challenges to creating change in practice, and the implications of improving the use of race and ethnicity data to identify and address gaps in health.

As the fastest-growing racial/ethnic group in the U.S., Asian Americans, Native Hawaiians, and Pacific Islanders (AANHPI) make up about 7% of the U.S. population. AANHPIs represent more than 50 ethnic groups and speak over 100 different languages, but national health data rarely collect information needed to disaggregate AANHPI into smaller groups to capture this diversity. This study reviewed AANHPI disaggregated data collection methods in large, U.S. population-based surveys, summarized disaggregated data reporting practices in the peer-reviewed literature, and conducted key informant interviews with 23 members of the National Network of Health Surveys who lead state/local and national surveys to identify challenges to data collection. We found that while large national surveys may include AANHPI subpopulation in data collection, sample sizes are often too small to permit meaningful data analysis. In our interviews, barriers cited included the lack of stakeholder interest, inadequate funding, lack of space on questionnaires, as well as methodologic challenges, such as the inability to obtain an adequate sample size and poor questionnaire design. Survey administration efforts to improve disaggregated health data are challenging, but when done, have led to a proliferation of new population-representative knowledge on the health of AANHPI subgroups. This study provides evidence of the population health value of disaggregating AANHPI data to identify the needs of vulnerable subpopulations. It also advances the field by offering key informants’ insights in overcoming implementation challenges.

BACKGROUND:Real-time, rapid assessment of barriers to care experienced by patients can be used to inform relevant health care legislation. In recent years, online communities have become a source of support for patients as well as a vehicle for discussion and collaboration among patients, clinicians, advocates, and researchers. The Breast Cancer Social Media (#BCSM) community has hosted weekly Twitter chats since 2011. Topics vary each week, and chats draw a diverse group of participants. Partnering with the #BCSM community, we used Twitter to gather data on barriers to care for patients with metastatic breast cancer and potential policy solutions. Metastatic breast cancer survival rates are low and in large part conditioned by time-sensitive access to care factors that might be improved through policy changes.OBJECTIVE:This study was part of an assessment of the barriers to care for metastatic breast cancer with the goal of offering policy solutions for the legislative session in California.METHODS:We provided 5 questions for a chat specific to metastatic breast cancer care barriers and potential policy solutions. These were discussed during the course of a #BCSM chat on November 18, 2019. We used Symplur (Symplur LLC) analytics to generate a transcript of tweets and a profile of participants. Responses to the questions are presented in this paper.RESULTS:There were 288 tweets from 42 users, generating 2.1 million impressions during the 1-hour chat. Participants included 23 patient advocates (most of whom were patients themselves), 7 doctors, 6 researchers or academics, 3 health care providers (2 nurses, 1 clinical psychologist), and 2 advocacy organizations. Participants noted communication gaps between patient and provider especially as related to the need for individualized medication dosing to minimize side effects and maximize quality of life. Timeliness of insurance company response, for example, to authorize treatments, was also a concern. Chat participants noted that palliative care is not well integrated into metastatic breast cancer care and that insurance company denials of coverage for these services were common. Regarding financial challenges, chat participants mentioned unexpected copays, changes in insurance drug formularies that made it difficult to anticipate drug costs, and limits on the number of physical therapy visits covered by insurance. Last, on the topic of disability benefits, participants expressed frustration about how to access disability benefits. When prompted for input regarding what health system and policy changes are necessary, participants suggested a number of ideas, including expanding the availability of nurse navigation for metastatic breast cancer, developing and offering a guide for the range of treatment and support resources patients with metastatic breast cancer, and improving access to clinical trials.CONCLUSIONS:Rapid assessments drawing from online community insights may be a critical source of data that can be used to ensure more responsive policy action to improve patient care.