Background Person-centeredness is a foundation of high-quality health systems but is poorly measured in low and middle-income countries. We piloted an online survey of four low and middle-income countries (LMICs) to identify the prevalence and correlates of excellent patient-reported quality of care. Methods We administered a cross-sectional online survey using Random Domain Intercept Technology to collect a sample of random internet users across India, Kenya, Mexico, and Nigeria in November 2016. The primary outcome was patient-reported quality of care. Covariates included age, gender, level of education, urban/rural residence, person for whom care was sought, type of provider seen, public or private sector status of the health facility, and type of facility. The exposure was an index of health system responsiveness based on a framework from the World Health Organization. We used descriptive statistics to determine the prevalence of excellent patient-reported quality of care and multivariable Poisson regression to calculate adjusted prevalence ratios (aPR) for predictors of excellent patient-reported quality. Results 14,008 people completed the survey (22.6% completion rate). Survey respondents tended to be young, male, well-educated, and urban-dwelling, reflective of the demographic of the internet-using population. 4,191 (29.9%) respondents sought care in the prior six months. Of those, 21.8% rated their quality of care as excellent. The highest proportion of respondents gave the top rating for wait time (44.6%) while the lowest proportion gave the top rating for facility cleanliness (21.7%). In an adjusted analysis, people who experienced the highest level of health system responsiveness were significantly more likely to report excellent quality of care compared to those who did not (aPR 8.61, 95% CI: 7.50, 9.89). In the adjusted model, urban-dwelling individuals were less likely to report excellent quality compared to rural-dwelling individuals (aPR 0.88, 95% CI: 0.78, 0.99). People who saw community health workers (aPR 1.37, 95% CI: 1.12, 1.67) and specialists (aPR 1.30, 95% CI: 1.12, 1.50) were more likely to report excellent quality than those who saw primary care providers. High perceived respect from the provider or staff was most highly associated with excellent ratings of quality while ratings of wait time corresponded the least. Conclusion Patient-reported quality of care is low in four LMICs, even amongst a well-educated, young population of internet users. Better health system responsiveness may be associated with better ratings of care quality. Improving person-centered care will be an important component of building high-quality health systems in these LMICs.

Various efforts to increase COVID-19 vaccination rates have been employed in the United States. We sought to rapidly investigate public reactions to these efforts to increase vaccination, including self-reported responses to widespread reduced masking behavior, monetary incentive programs to get vaccinated, and work vaccination requirements. Using a unique method for data collection (Random Domain Intercept Technology), we captured a large (N = 14,152), broad-based sample of the United States Web-using population (data collected from June 30 –July 26, 2021). About 3/4 of respondents reported being vaccinated. The likelihood of vaccination and vaccination intention differed across various demographic indicators (e.g., gender, age, income, political leaning). We observed mixed reactions to efforts aimed at increasing vaccination rates among unvaccinated respondents. While some reported that specific efforts would increase their likelihood of getting vaccinated (between 16% and 32%), others reported that efforts would decrease their likelihood of getting vaccinated (between 17% and 42%). Reactions differed by general vaccination intention, as well as other demographic indicators (e.g., race, education). Our results highlight the need to fully understand reactions to policy changes, programs, and mandates before they are communicated to the public and employed. Moreover, the results emphasize the importance of understanding how reactions differ across groups, as this information can assist in targeting intervention efforts and minimizing potentially differential negative impact.