Four essays on the economics of maternity care for health policy: evidence from Peru

Women's health outcomes and welfare in childbirth are intrinsically related to women's choice of provider and providers' staffing and decision making. But these factors have rarely been assessed empirically for a single population. This study is an attempt to do so in a systematic way. I use a unique and rich patient-level dataset that spans 2011 to 2015 from Lima, Peru, and use a variety of methods to address four research questions: a) conditional logit models to assess whether women trade-off continuity of care in midwife-led maternity units against structural quality in obstetrician-led maternity units; b) synthetic difference in difference methods to evaluate the policy of opening new midwife-led maternity units on use of health care and conditional logit for evaluating its effects on women's welfare; c) linear probability models with provider and obstetrician fixed effects to investigate whether the ratio of obstetricians/child deliveries on Sundays (when there are non-scheduled C-sections) afects the C-section rate upcoding by obstetricians and women's health outcomes, and d) instrumental variables methods to investigate the efect of C-sections on maternal health outcomes. I found that women that receive their antenatal care locally are prepared to travel further afield when it comes to giving birth, suggesting that they are prepared to accept less care continuity in midwife-led maternity units and incur higher travel costs to go to maternity units with better facilities. The introduction of new midwife-led maternity units increases women's welfare, but this efect is small compared with the investment required to set up the new midwife-led maternity units. New midwife-led maternity units have the potential to improve women's health outcomes only if they are close to an obstetrician provider and there are no midwife-led maternity units nearby. More obstetricians on duty on Sunday leads them to perform more C-sections and they justify this behaviour by up-coding comorbidities. The variation in the number of obstetricians has mixed effects on women's health outcomes improving some and worsening others. Finally, I show that C-sections are bad for women's health, especially causing haemorrhage in low C-section-risk women. (Haemorrhage is one of the main causes of maternal death in Latin American countries)

Eficiencia relativa en la producción de salud: América Latina 1996-2010.

Utilizando la metodología del Modelo Generalizado de Efectos Aleatorios Verdaderos propuesto por Fillipini y Greene (2014) con tratamiento Mundlak se encuentra que la eficiencia en la producción de salud estimada para 154 países durante los años 1996 al 2010 tiene la forma de U invertida, quedando al final del periodo aproximadamente en el mismo nivel. América Latina tiene el mismo patrón, la eficiencia relativa estimada mejora desde el año 1996 al 2003 y luego disminuye para, en el año 2010, ubicarse a un nivel inferior al año 1996. El primer trabajo que analiza la eficiencia en la producción de salud entre países es realizado por Evans Tandon, Murray y Lauer (2000), posteriormente Greene (2004) encuentra que los países son heterogéneos y propone utilizar el modelo Normal-Truncado y el de Battese y Coelli, entre otros. El presente trabajo considera seis modelos con resultados de eficiencia invariantes en el tiempo (dentro de los cuales se incluye los desarrollados por Evans y por Greene) cinco modelos con resultados de eficiencia variable en el tiempo, y dos modelos que diferencian eficiencia persistente y transitoria, todos ellos por aproximación paramétrica. Por el lado de la aproximación no paramétrica se consideran tres especificaciones de Data Evelopment Analysis (DEA) y adicionalmente el Índice de Malmquist. Se encuentra que los modelos con resultados invariantes en el tiempo, incluyendo los modelos DEA, no estiman adecuadamente la eficiencia persistente al no tomar en cuenta la heterogeneidad, mientras que los modelos que estiman eficiencia variable son consistentes. Por otra parte se evalúa los efectos de variables estructurales como el ingreso, el crecimiento económico y el índice de Gini, y variables de política como anemia, tuberculosis, VIH y malaria en la eficiencia estimada.Tesi

Health insurance system fragmentation and COVID-19 mortality: Evidence from in Peru

Peru has a fragmented health insurance system in which most insureds can only access the providers in their insurer’s network. The two largest schemes covered 53% and 30% of the population on 5 March 2020. Some individuals have dual insurance: they belong to both schemes and can thereby access a larger set of providers. We investigate whether this greater access to providers for those with dual insurance reduced mortality from COVID-19 between 6 March 2020 (the start of the pandemic in Peru) and 30 June 2021

Potential determinants of health system efficiency: Evidence from Latin America and the Caribbean

<div><p>This paper examines the levels of health system efficiency and their possible determinants across Latin American and Caribbean (LAC) countries using national-level data for those countries, as well as for other emerging and developed countries. The data are analyzed using data envelopment analyses and econometric advances that yield reliable estimations of the relationship between system efficiency and its potential determinants. We find that there is substantial room for efficiency improvements in the health system of most LAC countries. For example, LAC countries could improve life expectancy at birth by about five years on average at current public spending levels if they followed best practices. Furthermore, the paper assesses what factors amenable to policy act as the main possible levers for some countries to be able to translate a given level of health financing into better performance on access to care and health outcomes. Our econometric analyses suggest that efforts to increase health system efficiency could be focused in a few key policy areas associated with broader access to health services and better outcomes. These areas include general governance aspects, in addition to improvements in specific dimensions of the quality of health system institutions, notably stronger reliance on results-based management in the production of healthcare goods and services.</p></div

Addressing recall bias in (post-)conflict data collection and analysis : lessons from a large-scale health survey in Colombia

BACKGROUND: Much applied research on the consequences of conflicts for health suffers from data limitations, particularly the absence of longitudinal data spanning pre-, during- and post-conflict periods for affected individuals. Such limitations often hinder reliable measurement of the causal effects of conflict and their pathways, hampering also the design of effective post-conflict health policies. Researchers have sought to overcome these data limitations by conducting ex-post surveys, asking participants to recall their health and living standards before (or during) conflict. These questions may introduce important analytical biases due to recall error and misreporting. METHODS: We investigate how to implement ex-post health surveys that collect recall data, for conflict-affected populations, which is reliable for empirical analysis via standard quantitative methods. We propose two complementary strategies based on methods developed in the psychology and psychometric literatures-the Flashbulb and test-retest approaches-to identify and address recall bias in ex-post health survey data. We apply these strategies to the case study of a large-scale health survey which we implemented in Colombia in the post-peace agreement period, but that included recall questions referring to the conflict period. RESULTS: We demonstrate how adapted versions of the Flashbulb and test-retest strategies can be used to test for recall bias in (post-)conflict survey responses. We also show how these test strategies can be incorporated into post-conflict health surveys in their design phase, accompanied by further ex-ante mitigation strategies for recall bias, to increase the reliability of survey data analysis-including by identifying the survey modules, and sub-populations, for which empirical analysis is likely to yield more reliable causal inference about the health consequences of conflict. CONCLUSIONS: Our study makes a novel contribution to the field of applied health research in humanitarian settings, by providing practical methodological guidance for the implementation of data collection efforts in humanitarian contexts where recall information, collected from primary surveys, is required to allow assessments of changes in health and wellbeing. Key lessons include the importance of embedding appropriate strategies to test and address recall bias into the design of any relevant data collection tools in post-conflict or humanitarian contexts

Health insurance system fragmentation and COVID-19 mortality : Evidence from Peru

Peru has a fragmented health insurance system in which most insureds can only access the providers in their insurer’s network. The two largest sub-systems covered about 53% and 30% of the population at the start of the pandemic; however, some individuals have dual insurance and can thereby access both sets of providers. We use data on 24.7 million individuals who belonged to one or both sub-systems to investigate the effect of dual insurance on COVID-19 mortality. We estimate recursive bivariate probit models using the difference in the distance to the nearest hospital in the two insurance sub-systems as Instrumental Variable. The effect of dual insurance was to reduce COVID-19 mortality risk by 0.23% compared with the sample mean risk of 0.54%. This implies that the 133,128 COVID-19 deaths in the sample would have been reduced by 56,418 (95%CI: 34,894, 78,069) if all individuals in the sample had dual insurance