Data Triangulation Applications in Population and Health Programs- -The Global Experience

What is “Triangulation”? Triangulation is a technique used to identify the location of an object by linking measurements taken from two known points. It uses basic equations in trigonometry to measure the distance of an object from the two points and its co-ordinates. This tool was initially used in navigation, especially to measure the distance of a ship from the shore and its exact location. More than two measurements can be used to pinpoint the location in space. The GPS, based on measurements via a number of satellites, is a good example. In general, the concept of triangulation can be utilized to use two or more pieces of information to draw inferences about a desired characteristic. This is possible if the underlying variables are structurally linked.

Data Triangulation Data triangulation is “synthesis and integration of data from multiple sources” for arriving at inferences. The concept of triangulation is applied to data where two or more sources are used to estimate a desired parameter. The sources may be of different types: service statistics, census, surveys, findings of past research, quantitative as well as qualitative. Data triangulation has become a useful tool for program decision making and is extensively used in monitoring and evaluation of health programs and also in various other fields. It can give quick results at a relatively low cost. But it calls for a comprehensive knowledge of the processes being studied and awareness of various data sources.

Why is data triangulation needed? Health and other programs need estimates of certain indicators for monitoring and evaluation. The data to obtain such indicators may not be available from a single source, or be too expensive to collect, or the process too time- consuming. Some of the indicators can, in principle, be obtained from household surveys. But these take a long time to plan, to select samples, to collect and process data. This is unsatisfactory for program monitoring. If the sample size is large, the cost is heavy; besides, the operations can take a long time. Large surveys are quite expensive and can not be carried out frequently. For small samples, the sampling error is large. This makes it difficult to assess moderate changes or differentials. In retrospective surveys, the quality of data is affected by recall lapse.

How does it work? In many situations, some data are readily available from the program records or service statistics. These by themselves may not be adequate to give an idea of functioning of the program but can be combined with other information to estimate desired indicators. Many indicators are quite simple, obtained as ratios or proportions. The service statistics may give the numerator but not the denominator that can be computed from other information. Some of the required quantities can be obtained from the census, population projections, baseline surveys, results from large national surveys etc. This may require extrapolation, adjustments, and prior knowledge of certain processes especially theoretical and empirical relationships. Concordance among data from various sources strengthens confidence in the information. Linkage of variables obtained from different sources facilitates studying cause-effect relationships.

The Steps- General Issues First, the key questions must be listed and the parameters required to answer these identified. These could expressed in terms of clearly defined measures. The data sources for various terms required to compute the estimate should be explored. It should be seen if the required data are either readily available or can be collected easily. If this is not possible, there is a need to see if data are available for some of the terms directly but from different sources. These could then be pooled for triangulation. If direct data are not available, one could explore whether for some of the terms information can be obtained by induction. This calls for a knowledge of relationships, gathered from prior research.

The Steps- Specific Issues If there are multiple sources for a characteristic and these give nearly the same results, one would have greater confidence in the validity. Else, the best source should be used after assessing quality (for example, in India, vital rates are available from the civil registration and the sample registration system, but for most of the states, the SRS estimates are considered more accurate). If the precise term is not available, it could be explored if it can be computed from two or more available terms (for example, the expected number of births can be computed from projected population and birth rate). If the reference date for the source does not match the reference for the indicator, appropriate tools of interpolation could be used. Recent trends could be utilized for short-term extrapolation. Some of the indicators could be refined using knowledge about the processes and quality of data (for example, if it is known that the service statistics as reported are inflated, a correction can be made based on results from special small studies).

Ex.1. Institutional Delivery Care The question : How wide is the coverage of institutional delivery care? The indicator: Percent of all deliveries in the area that are conducted in institutions = 100 X No. of deliveries in institutions Total number of deliveries Data Sources: No. of deliveries in institutions- Reports from institutions Total no. of deliveries- Not easily available. Use expected no. of deliveries instead This can be estimated from total estimated population and the crude birth rate (CBR). Estimated population is available from population projections and CBR from the SRS (taken as more reliable than civil registration). Thus, three sources are used; service statistics, population projections and SRS.

Ex. 1. Computations No. of institutional deliveries reported in one year (from service statistics) = 629,780 Population at mid-year (from RG’s population projections) = 64,077 thousand Crude birth rate (CBR- from SRS) = 28.3 per thousand Estimated births in one year = x28.3/1000 = 1,813,379 Percent Covered = 100* / = This estimate can be refined. The CBR given above was for 2006, but the other data refer to CBR could have declined in one year. From the recent trends, it is seen that the annual decline was 0.45 on average. Then the CBR for 2007 should be taken as and the coverage works out to 35.3 %. Here, additional information, on trends in CBR is also used.

Ex.2: Ante-natal care The question : How wide is the coverage of antenatal care? The indicator: Percent of pregnancies in the area that received antenatal care = 100* No. of pregnancies received antenatal care Total number of pregnancies Data Sources: No. of pregnancies received antenatal care- Reports from institutions (based on ANM and PHC reports; assuming that these are compiled by PHCs and reported), Total no. of pregnancies- Not easily available. Use expected no. of pregnancies instead. But this should be recognizable pregnancies. This can be estimated from total estimated births adjusted by a factor since all pregnancies do not result in live births. A factor of 1.15 is used based on earlier knowledge (assuming that 115 pregnancies result in 100 live births)

Ex.2: Computations No. of pregnancies that received antenatal care in one year (from service statistics) = Estimated births in one year (computed earlier) = 1,813,379 [Estimated no. of births is computed from the estimated mid-year population, available from population projections, and CBR from the SRS.] Estimated recognizable pregnancies = 1.15 * = [the factor 1.15 is taken from previous information] Percent Covered = 100* / = 75.8.

The Global Experience The technique of triangulation is increasingly being used in assessment of health and other programs around the world. For example, it has been used in studying changes in HIV prevalence and possible causes of these. In Botswana, trends in HIV prevalence among pregnant women (ANC Sentinel Surveillance) were compared to trends in mortality due to AIDS (from Vital Statistics), condom use (from Behavior Surveillance Survey), and numbers of clients in ART and PMTCT sites (from Service Statistics). These showed a good concordance allowing a confirmation of the decline in HIV prevalence and the linkages. This is a good example where various trends were juxtaposed to arrive at an inference.

Points to be noted Triangulation is used when direct data are neither readily available nor can be collected in a reasonably short time and at a low cost. It pools information from various sources, often of different kinds. There is a need to be parsimonious in choosing the appropriate data from a multitude after assessing quality. Knowledge of interrelationships is essential to achieve triangulation. This technique can give results at a low cost and in a reasonable time. The results depend on how good the data are and the how valid are the assumptions made regarding interrelationships. This a very useful tool for program monitoring, evaluation, and reformulation. Triangulation can also be a valuable technique in assessing impact.