• UNDG
  • UNDG
  • UNDG
Silofighters: the UNDG blog

Mining alternative data: What national health insurance data reveals about diabetes in the Maldives

BY Yuko Oaku | 07 November 2018

An island nation consisting of 1,190 small islands, the Maldives is clustered around 26 ring-like atolls spread across 90,000 square kilometers. For many centuries, the Maldivian economy was entirely based on fishing. Tuna is one of the essential ingredients in the traditional dishes of the archipelago. But between 1980 and 2013, the GDP per capita increased from $275 to $6,666 due to the success of the high-end tourism sector. With the rapid economic growth and a wave of globalization, there have also been changes in the dietary preferences and lifestyles of Maldivians. A staggering 30 percent of the Maldivians are overweight due to unhealthy diets and lack of physical activity, according to data from the Global Health Observatory.   Consuming sugary beverages is also a big problem among Maldivian youth and young adults. According to a study by the World Health Organization, in 2015, 4.7 million litres of energy drinks were imported to the Maldives, which is a very high volume for such a small population (around 410,000 people live in the Maldives). These unhealthy habits are drivers for the increase in non-communicable diseases, such as cardiovascular, cerebrovascular and hypertensive disease.  These diseases are the main causes of death among Maldivians. According to the National Health Statistics from 2014, diabetes is ranked as the ninth overall cause of death in the Maldives. [caption id="attachment_10393" align="alignnone" width="450"] "Drinking energy drinks is not cool" Health Protection Agency Maldives[/caption] Analyzing the prevalence of Type II diabetes with Insurance Data All Maldivian nationals are covered under the Government’s universal health insurance plan called “Aasandha”. Since it began its services in 2012, the plan gives full coverage to all health services from most health care providers and up to a certain amount for some of the private health care providers. The plan also covers care in affiliated hospitals in neighboring India and Sri Lanka in case the treatment is not available in the Maldives. Aasandha data provides personal data records and insurance data for all Maldivians. Since the usual data source for non-communicable diseases is the Demographic and Health Surveys, which is carried out every 6 years (most recently in 2015 and before that in 2009), we thought we could get more up-to-date data on diabetes if we looked directly at the health insurance data. Our team assumed that analyzing this data would serve as proxy indicators for the SDG indicators 3.8.1: Coverage of essential health services. Initially, this indicator was labeled as Tier 3 indicator, meaning that no internationally established methodology or standards were yet available for the indicator. As of 11 May 2018, however, 3.8.1 has been upgraded to Tier 2 indicator, which means that the indicator is conceptually clear, has an internationally established methodology and standards are available, but data are not regularly produced by countries. Our idea was to have an anonymized look at the data from the universal health insurance plan to see what else we could learn about non-communicable diseases. We at the UN Country Team in the Maldives, UNDP and WHO, partnered with the Maldives National University (MNU) research team and with the National Social Protection Agency (NSPA), the custodian of Aasandha service in the Maldives. What we found out about Type II diabetes in the Maldives: We dug into the anonymized health care records for 2016, including information about: 1) what diseases the Aasandha coverage is used for 2) the cost 3) where the medical procedures take place Together with the research team, we decided to focus on Type II diabetes for the scope of this study. We found some interesting facts about the prevalence of Type II diabetes in the Maldives: More than 3 out of every 5 people who have diabetes are women. The mean age of patients with Type II diabetes is 57, while the youngest age is 13. Females get diagnosed with Type II diabetes at a younger age compared to men and there is a relationship with gestational diabetes. Of those seeking care, 79 percent of the people go to private health care providers, whereas only 21 percent seek services from public health care providers. We also discovered that the Aasandha data was also incomplete. For instance, there were missing records from some of the largest regional hospitals in most populated atolls in the country. This may suggest that data from government hospitals are not entered into the system because patients don’t need to make a claim for the payment, whereas in private hospitals, the data is needed to allow patients to make a claim for their payment. It could be that more people are using public health care providers, but since the data is not entered into the Aasandha system,this information is unavailable to us. [caption id="attachment_10395" align="alignnone" width="393"] WHO Maldives[/caption] Next frontiers in proof of concept for alternative data With this pilot study we found some interesting facts about the prevalence of Type II diabetes in the Maldives as well as some possible data gaps in the Aasandha insurance data. We will be sharing our findings and challenges of using Aasandha data with the members of the UN Country Team as well as relevant ministries and agencies, including the Ministry of Health and the National Social Protection Agency. Reflecting on this pilot study, we will continue to support the country to explore alternative sources of data that will enable us to track more SDG indicators in the Maldives. According to an internal assessment done on data availability for all SDG indicators by the National Bureau of Statistics, there’s currently no mechanism for data generation for 56 indicators and for another 51 indicators, additional efforts will be required to make the data available. With all this data missing, we’ll need to tap into additional resources to make the data available because if we don’t know where the Maldives stands on Sustainable Development indicators, it’ll be hard to plan to achieve them. There is definitely a need for new data sources and having this data gap in mind, we have another pilot project in the works that’s going to use call detail records data to track population mobility to the urban centers of Male. Stay tuned for more in our work mining alternative data sources for the Maldives!

Silofighters: the UNDG blog

Untangling the complexity of the Sustainable Development Goals in Moldova

BY Ana Moraru, Valeriu Prohnitchi | 01 November 2018

The Sustainable Development Goals (SDGs) are a beautiful vision for a better world, where people have equal access to food, health, public services, education, equal rights and pay. It’s a world where oceans and air are clean, fish are happy, and forests are preserved. Comprehensive? Certainly. Complex? Beyond any doubt. No matter how you want to see it, the stakes for achieving the SDGs are high. The clock is ticking. How are governments going to make the SDGs happen in the next 12 years? How do policy makers translate these goals into real outcomes for people? Look at things differently: think systems At first glance, it might be tempting to eat the elephant one bite at a time. The standard approach for analysis is to decompose phenomena into manageable pieces, which can be easier to grasp. The puzzle is then solved when all the pieces are put together. With this approach, the whole equals to the sum of the parts. However, this is not the optimal strategy in complex systems, where a standard approach would only encourage silo-based and piecemeal solutions. In complex systems, the uncoordinated actions of actors would result in suboptimal outcomes for the whole systems. Let’s take Goal 1: No Poverty, for example. How do we expect to achieve Goal 1 without touching upon Goal 3: Good Health and Well-Being or Goal 4: Quality Education? At the UN in Moldova, we looked at the Global Goals from a different lens, that of multiple causes, effects, feedback loops, and actors. With such an approach, the whole may equal more than the sum of all parts.  Our hypothesis was this: by uncovering the fundamental causal loops and relations among the SDG targets, we can help the government and the UN in Moldova identify the “leverage points” – policy priority areas. In turn, this will help us to make progress over multiple goals at once, and prioritize policy actions and investments of scarce resources in the short, medium, and long term. In our last blog post, we shared our experiences working with the government in ‘Glocalizing’ the Sustainable Goals in Moldova. After that exercise, we supported the Republic of Moldova to update the national strategic planning framework to encompass the SDGs. During the early stages of the strategy development, the apple of discord happened to be a persistent one: among of the many development challenges, how should the government decide which ones to prioritize? Once again, the systems analysis perspective came in handy. If we look at SDG targets from the perspective of systems dynamics, we can analyze the connections and the causal and feedback loops among them. Some targets will even prove to be more connected than others; progress on these targets would most likely generate a multiplier effect. We like to call these “SDGs accelerators”, or “leverage points”. If we attained progress on these accelerators, then we would help the country progress on the Global Goals as a whole. This was our theory of change. It takes a village to raise a child (or a country) One of the criticisms of the national strategic planning policies is that these don’t reflect the needs of the people and vulnerable groups. This time, as we say in Moldova, we tried to avoid stepping on the same rake twice. We convened different players, including representatives of ministries, MPs, governmental agencies, civil society organizations, including representatives of vulnerable groups, academia, donors, and development agencies. This process helped us to reach a common vision and understanding that helped us set the priorities for the actual National Development Strategy. The systems thinking approach was helpful, yet again. What we did to untangle the SDGs To untangle and analyze the SDGs, we used the stock-and-flow and causal links diagrams, an approach from the field of System Dynamics developed at MIT. Within this analytical framework, a stock of some elements varies due to inflows increasing the stock and outflows diminishing it. What does this mean exactly? Let’s take Goal 3: Good Health and Well-being (see Figure 1). The rectangle “Healthy People” denotes a stock variable. In systems dynamics, stock variables represent variables that accumulate or that can be depleted. To better understand how it works, imagine a bathtub.  The inflow from the “Wellness Rate” increases the stock of “Healthy People”, while the outflow of “Disease Rate” or “Accidental Death Rate“ will decrease the stock because more healthy people will get sick due to diseases or die of accidents over time. Arrows represent a primary or secondary causal direction moving from a cause to an effect. Figure 1. SDG3: stock-and-flow causal loop diagram Source: Moldova SDGs system map. The solid lines conventionally denote that the cause and effect move in the same direction holding all else constant; e.g. an increase in the “3.8 Universal Healthcare Coverage” will cause an increase in the “Treatment Rate”. A dashed line denotes the cause and effect moving in the opposite directions, e.g. an increase in 3.3 Communicable Disease Reduction will decrease the Disease Rate. In the SDGs complexity mapping, the first major decision was where to begin. In our case, we started with the SDG1: No Poverty, for which we have conducted a prima facie analysis of the immediate causal links (Figure 2). The central stock we are trying increase is “People with Good Quality of Life”. People move from the stocks of “People in Extreme Poverty” to “People in Poverty” and then to “People with Good Quality of Life”, following the flows of 1.1 Extreme Poverty Eradication Rate and 1.2 Poverty Elimination & Quality of Life Improvement Rate. We then add a new layer of analysis, by incorporating the SDG2: Zero Hunger. The target 2.4 “Resilient Agricultural Practices” shifts the “Vulnerable Food Production” towards “Resilient Agriculture Food Production”. Further on, an increase in “Resilient Agriculture Food Production” will help raise 2.3 “Agricultural Productivity & Incomes of Small-scale Food Producers”, which in turn increases 10.1 “Income Growth of Bottom 40 percent”. Therefore, the resilient agriculture looks like an important poverty reduction strategy and achieving SDG 2: Zero Hunger helps achieving SDG 1: No Poverty having SDG 10: Reduced Inequalities as intermediary. Layer after layer, we arrive at a densely packed map revealing the most essential mutual influences among the Moldovan SDGs targets and related policies. From this comprehensive exercise, we narrowed down the common vision for Moldova in 2030 to three main poles: People with a good quality of life, with decreased emigration and progressive values, have to be put at the centre of the development vision – i.e. development should be for the people rather than by the people. Effective, accountable and inclusive institutions able to put an end to corruption are essential for unleashing the potential existing in the wider society. Sustainable production and sustainable industrialization is the most promising economic model enabling a decisive and lasting reduction in poverty and in providing equal opportunities for all to achieve high standards of living. We found some answers and have more questions Overall, systems analysis proved to be a great method for looking at the big picture. It helped identify the most connected elements which served as a basis for defining the development vision for the National Development Strategy Moldova 2030 and for prioritizing key areas of intervention. As such, we made the first step towards understanding the causal links between SDG targets. However, what we couldn’t see is how these links reproduce over time. The next step in this analysis would be to check how these links change over time, allowing us to understand the dynamics of the system. Similarly, we would want to see the strength of the links to understand the magnitude of influence. This would represent a highly ambitious exercise, requiring a different time-frame and more solid data. Are you trying using a similar approach to untangle the Sustainable Development Goals? Share with us!

DELIVERING AS ONE ON THE 2030 AGENDA:
OUR STANDARD OPERATING PROCEDURES

You Tube
Mainstreaming, Acceleration and Policy Support: The UNDG MAPS approach to achieve the 2030 Agenda
5 July 2016
UNDG members on Twitter
UNDG on Twitter

UN AT COUNTRY LEVEL

Note: The boundaries and names shown and the designations used on the map do not imply official endorsement or acceptance by the United Nations.