PRINCETON (US)—A program created to provide health care to 50 million Mexicans has been shown effective at reducing catastrophic costs, according to the largest health policy study of its kind. The success of Seguro Popular, which covers about as many people as are uninsured in America, could provide lessons for other countries, according to the study authors.
“We were able to scientifically establish that the program achieved its main goal to reduce health care costs,” says Kosuke Imai, assistant professor of politics at Princeton University who developed a new statistical method for the study. “This represents an important success not only for health care but also for a larger agenda to encourage evidence-based policymaking.”
The new statistical method, says Imai, enables more accurate and efficient evaluation, and is now being implemented or considered for evaluations of many other public policy programs around the world.
“The success of Seguro Popular in reducing catastrophic health expenditures is remarkable, not least because governmental money spent on the poor in many countries rarely reaches the intended recipients,” says Gary King, the lead author on the study and the David Florence Professor of Government and director of the Institute for Quantitative Social Science at Harvard University.
Voluntary enrollment in Seguro Popular, provided at no cost to the poor, offers access to health clinics, medications, regular and preventive medical care, and the money to pay for it. The program’s primary goal is the reduction of catastrophic health expenses—costs that exceed one-third of a household’s yearly disposable income.
“In addition to its substantive conclusions, the research offers new insights into how to evaluate policy programs,” Imai says. “The challenge for program evaluation is the cost of following up on results for a large number of individuals.”
The study monitored health outcomes and expenditures in 118,569 households over 10 months. In order to select comparable groups, the researchers paired up 174 communities based on similarities in background, such as how healthy its inhabitants were, the size of its population and the number of schools that were located there. One community within each pair was randomly chosen to receive treatment. Families in the treatment community were encouraged to enroll in Seguro Popular, health facilities were built or upgraded, and medical personnel, drugs and other supplies were provided. Families in the other community did not receive any change in their health care resources.
Researchers found this “matched pair” design decreased the margin of error to as little as one-sixth of what it would be with traditional experimental methods. In other words, researchers can have more confidence in conclusions with fewer individuals and communities. “The power of pair-matching is incredible,” Imai adds.
The research was funded by the Mexican Ministry of Health, the National Institute of Public Health in Mexico, and the Harvard Institute for Quantitative Social Science.
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