Development of summary indices of antenatal care service quality in Haiti, Malawi and Tanzania.

INTRODUCTION: Measuring quality of care in low-income and middle-income countries is complicated by the lack of a standard, universally accepted definition for 'quality' for any particular service, as well as limited guidance on which indicators to include in measures of quality of care, and how to incorporate those indicators into summary indices. The aim of this paper is to develop, characterise and compare a set of antenatal care (ANC) indices for facility readiness and provision of care. METHODS: We created nine indices for facility readiness using three methods for selecting items and three methods for combining items. In addition, we created three indices for provision of care using one method for selecting items and three methods for combining items. For each index, we calculated descriptive statistics, categorised the continuous index scores using tercile cut points to assess comparability of facility classification, and examined the variability and distribution of scores. RESULTS: Our results showed that, within a country, the indices were quite similar in terms of mean index score, facility classification, coefficient of variation, floor and ceiling effects, and the inclusion of items in an index with a range of variability. Notably, the indices created using principal components analysis to combine the items were the most different from the other indices. In addition, the index created by taking a weighted average of a core set of items had lower agreement with the other indices when looking at facility classification. CONCLUSIONS: As improving quality of care becomes integral to global efforts to produce better health outcomes, demand for guidance on creating standardised measures of service quality will grow. This study provides health systems researchers with a comparison of methodologies commonly used to create summary indices of ANC service quality and it highlights the similarities and differences between methods.

Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: a time-series analysis.

BACKGROUND: Hurricane Maria struck Puerto Rico on Sept 20, 2017, devastating the island. Controversy surrounded the official death toll, fuelled by estimates of excess mortality from academics and investigative journalists. We analysed all-cause excess mortality following the storm. METHODS: We did a time-series analysis in Puerto Rico from September, 2017, to February, 2018. Mortality data were from the Puerto Rico Vital Statistics System. We developed two counterfactual scenarios to establish the population at risk. In the first scenario, the island's population was assumed to track the most recent census estimates. In the second scenario, we accounted for the large-scale population displacement. Expected mortality was projected for each scenario through over-dispersed log-linear regression from July, 2010, to August, 2017, taking into account changing distributions of age, sex, and municipal socioeconomic development, as well as both long-term and seasonal trends in mortality. Excess mortality was calculated as the difference between observed and expected deaths. FINDINGS: Between September, 2017, and February, 2018, we estimated that 1191 excess deaths (95% CI 836-1544) occurred under the census scenario. Under the preferred displacement scenario, we estimated that 2975 excess deaths (95% CI 2658-3290) occurred during the same observation period. The ratio of observed to expected mortality was highest for individuals living in municipalities with the lowest socioeconomic development (1·43, 95% CI 1·39-1·46), and for men aged 65 years or older (1·33, 95% CI 1·30-1·37). Excess risk persisted in these groups throughout the observation period. INTERPRETATION: Analysis of all-cause mortality with vital registration data allows for unbiased estimation of the impact of disasters associated with natural hazards and is useful for public health surveillance. It does not depend on certified cause of death, the basis for the official death toll in Puerto Rico. Although all sectors of Puerto Rican society were affected, recovery varied by municipal socioeconomic development and age groups. This finding calls for equitable disaster preparedness and response to protect vulnerable populations in disasters. FUNDING: Forensic Science Bureau, Department of Public Safety, and Milken Institute School of Public Health of The George Washington University (Washington, DC, USA).