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Using Data in Public Health Delivery

Data is essential to reliable and valid public health research. However, data from studies will only be useful if used, analyzed, and applied in a timely manner. Data can be used to evaluate program impact, to determine appropriate public health interventions, to monitor progress, to determine populations to target for an intervention, to determine barriers to care, and to influence public policy.

Data For Evaluating Program Impact

Data is critical to evaluate the effect or impact of a program. For instance, a study conducted at Kilimanjaro Christian Medical Centre (KCMC) measured the rates of children attending follow-up appointments after pediatric cataract surgery before and after an intervention was implemented. The study found that before the intervention, in 2003-2004, 154 children had cataract surgery at KCMC. Of those children, 67% came for their 2-week postoperative follow-up appointment, while only 43% came for their 10-week follow-up appointment.  Since cataract surgery alone will have limited value if follow-up care is poor, the medical center implemented specific changes to improve follow-up. A high-quality counseling service was implemented, and a tracking system was developed which recorded each child’s next scheduled follow-up appointment and contact information. If a child did not appear for a scheduled follow-up appointment, then a parent or contact person was called. In 2006, 185 children had cataract surgeries, and post-intervention data showed that 89% of children came for their 2-week follow-up appointment, while 83% came for their 10-week follow-up.(1) Upon comparing the %age of children in 2006 who attended their follow-up appointments to the %age of children who went in 2004, the data shows a 22% increase for the 2-week follow-up and a 40% increase for the 10-week follow. Therefore, the data from this study demonstrates how the intervention was successful in improving postoperative follow-up rates of pediatric cataract surgery.   Since this was not, however, a randomized control study, it is also important to realize that other factors might have impacted the study results.  For example, did transportation in the region improve and enable the children to attend their follow-up appointments?  Could weather conditions have changed that could have influenced the follow-up rates?  If there had been a control group that was assessed concurrently with the intervention group, then the researchers and program implementers would have been able to conclusively determine whether the intervention led to the improved follow-up retention.

VillageReach is an international NGO that works to improve access to healthcare for remote, underserved communities and has used data to evaluate their program impact. The organization launched a 5-year pilot project to ensure prompt and universal access to vaccines in Cabo Delgado, a province in northern Mozambique. Researchers conducted a comprehensive evaluation analyzing the impact of VillageReach’s pilot project in Cabo Delgado. The evaluation found that VillageReach’s project increased immunization rates from 68.4% to 95.4%, reduced vaccine stockouts to 1% in 2006 compared to almost 80% in 2004, and improved training and supervision for health center staff.(2) Furthermore, “attributing these impacts to the Project is supported by the comparisons to Demographic and Health Surveys data from 1999 and 2003, other administrative data, and the fact that vaccination coverage rates in the neighboring province of Niassa, where the project did not undertake any activities, were significantly lower than those found in Cabo Delgado for a similar time period. In addition, no other organizations were working to improve vaccination coverage in all districts of Cabo Delgado during the project period.” (3) By analyzing the data from the outcome assessment, it is evident that the program has helped to increase vaccination rates in Mozambique.

Data can also be used to demonstrate that an intervention is not effective or does not have its intended result. For example, an 8-year study investigated whether a low-fat diet high in fruits and vegetables would reduce cardiovascular disease. Though researchers hypothesized that a low-fat diet would reduce the incidence of cardiovascular disease, their data showed otherwise. Of the 19,541 women assigned to eat a low fat diet, 0.63% developed coronary heart disease, while 0.28% developed stroke. Of the 29,294 women assigned to a control group, 0.65% developed coronary heart disease, while 0.27% developed stroke. The data shows very little difference between the %age of women who developed coronary heart disease and stroke in the control group and low fat diet group. Thus, the data from this study led the researchers to conclude that a dietary intervention that reduced total fat intake did not significantly reduce risk for cardiovascular disease and that other lifestyle and dietary interventions need to be studied.(4)

Data For Appropriate Public Health Interventions

Cervical cancer rates vary worldwide, though it is estimated that 85% of the cases occur in low-income or middle-income countries.  Similarly, it is estimated that 85% of deaths from cervical cancer occur in developing countries.(5)  This variance is largely attributable to developing countries’ lack of infrastructure and financial resources to establish a cytolog screening program. Therefore, a study was implemented to determine if visual inspection with acetic acid, which is a more cost effective and appropriate intervention for developing countries, would provide results comparable to more advanced technological screening methods.  The study determined the sensitivity, specificity, and positive predictive value of visual inspection with acetic acid and colposcopy in order to compare the effectiveness and accuracy of the interventions to detect cervical cancer. Probabilities such as sensitivity and specificity are important to consider when comparing tests and analyzing data.  Sensitivity is the probability that a test gives a positive result when a disorder is present.  Specificity is the probability that a test gives a negative result when the person tested is healthy. The positive predictive value is the probability that a disorder is present when the test gives a positive result (the number of people correctly diagnosed as positive divided by the total number of true positives and false positives).”(6)

The study found that visual inspection with acetic acid, a low-tech screening method, had a sensitivity of 71% for detecting CIN II (moderate dysplasia) or a worse condition, while the specificity was 74%, and the positive predictive value was 11%. On the other hand, colposcopy had a sensitivity of 81%, a specificity of 77%, and a positive predictive value of 14% for CIN II or worse. The data from this study reveal how visual inspection with acetic acid and colposcopy have similar sensitivities and specificities. The researchers, therefore, concluded that “increased technology in itself does not guarantee significant improvement in detection of disease…. Findings such as this are empowering, because in developing countries, technical supplies are the dominant component of the cost structure… When compared with other screening options (eg, Papanicolaou smears or human papillomavirus tests), visual inspection requires fewer technical supplies.” (7) By comparing the sensitivity and specificity rates of these data, it is evident that “the benefit of an inexpensive point-of-care diagnosis and treatment algorithm will be a powerful incentive to pursue visual inspection for cervical cancer screening in developing countries.” (8) Thus, the data from this study were used to show how visual inspection with acetic acid and high-tech screening methods have similar abilities to detect cervical cancer and its pre-cancerous stages.  It is important to note, however, that neither test provides conclusive results of 100% specificity and sensitivity.

Data Monitors Progress

Data is also needed and can be used to monitor progress towards a goal or target. For example, for the Millennium Development Goals, accurate and up-to-date data is essential in order to record progress and determine what countries are on track to meet the goals.  Data from a World Health Organization publication demonstrated the progress that has been made towards achieving Millennium Development Goal 4, which aims to reduce by two thirds the under 5 mortality rate between 1990 and 2015. The report found that child mortality continues to fall, and in 2008, the total annual number of deaths in children under 5 fell to 8.8 million.  This represents a 30% decrease from the 12.4 million estimated in 1990. Though this demonstrated decrease in mortality rate is encouraging, the data also illustrates the need for public health efforts to continue focusing on combating child mortality since a 30% decrease is far from the goal’s target of a 66.7% decrease.(9)

Using Data To Target Population-Based Interventions

A 1995 National Survey of Family Growth found that the % of U.S. women who gave birth before the age of 18 varies by income level and race. Those who made under $20,000/year had a 21% pregnancy rate, while those who made between $20,000-49,999 had a rate of 9%.  Those who made above $50,000 had a rate of 3%. This study also found that, in general, 5% of non-Hispanic white women gave birth before the age of 18, while 18% of non-Hispanic black women gave birth before the age of 18.(10) Thus, the data demonstrates that women from lower socioeconomic backgrounds and black women have particularly high rates of birth before the age of 18.  Information from this data can be utilized to target new interventions to specific populations.

Data is also critical for determining which groups of people have the highest surgical need. Though most of the global burden of surgical disease falls among the world’s poorest, only a small %age of surgical interventions occur in lower income countries. Research shows that of the 234.2 million major surgical procedures performed worldwide annually, the poorest third of the world’s population only received 3.5% of the surgical interventions.(11)  In addition, countries that spend less than $100 per person on health care were found to have an estimated rate of major surgery of 295 procedures per 100,000 people per year.  In contrast, countries that spend more than $1,000 have a rate of 11,110 procedures per 100,000 people per year.(12) This data documents the lack of surgery in poor countries, and reveals the need to make surgical interventions more accessible  to the poor in developing countries. Though the data demonstrate the tremendous disparity that exists, the data also show the potential improvements that could be realized if such inequity was addressed. (13)

Data can also be used to show where insecticide-treated malaria nets (ITNs) for children are most needed. Though great improvements in insecticide-treated malaria nets coverage have been documented, there are still specific countries and areas which have a large demonstrated need. In 2000, only 2.3 million (1.8%) African children living in stable malaria conditions were protected by an ITN. The number increased to 20.3 million (18.5%) in 2007, leaving 89.6 million children unprotected. Of these unprotected children, 54% were living in only seven African countries (Nigeria, Demographic Republic of Congo, Uganda, Sudan, Mozambique, Côte d’Ivoire, and Cameroon), and 25% were in Nigeria alone. This data suggest that attention should be targeted to increased use of ITNs in these areas ofAfrica. The data also reveals that 25.5% of children living in areas with no malaria risk were protected by ITN, while only 18.5% of those living in areas with stable malaria risk were protected by ITN.  The data indicates that coverage rates are lowest in areas with the highest risk of malaria transmission (where they are needed most) , and improved targeting might therefore be required to reduce this disparity.(14)

Data Determines Barriers to Care and Reveals Patient Perceptions

In order to design interventions that will have the greatest impact, it is important to determine barriers to care and to assess patient perceptions.  The Indian organization 1298 used data to determine barriers to care.  Developed in Bombay, India, 1298 strives to provide high quality ambulance services, and the organization utilizes a sliding-scale method to determine fees. Those who elect to be transported to the public hospital for treatment do not have to pay at all. However, the organization realized that India’s poorest people were still not utilizing their services as much as other richer segments of the population. Therefore, in order to understand why 1298 was not reaching those living below the poverty line, researchers conducted 100 one-on-one interviews in Mumbai slums. They found that 49% of the people interviewed would not call 1298 because it cost too much, 19% did not know the number, and 14% said that it took too long for an ambulance to arrive. In addition, as of 2009, 60% of respondents said they would take a rickshaw to the hospital, while only 15% would take an ambulance. This data shows that in order for 1298 to increase their uptake rates among India’s poorest, they should focus on making it clear that their services are free for those who can’t afford them.  Additionally, the data revealed that the phone number needed to be advertised.(15)

Data and Public Policy

As demonstrated by needle exchange programs, data can be used to influence public policy and to demonstrate the need or potential impact of a policy.   There are a variety of transmission modes for HIV.  In Russia, 83% of HIV infections come from needle sharing.  In Ukraine, 64% of HIV infections occur from needle sharing, while the %ages are 74% in Kazakhstan, and 72% in Malaysia. In the United States, needle sharing directly accounts for more than 25% of AIDS cases. In order to prevent needle sharing, there is a proven solution: needle exchange programs which provide injectors with clean needles in exchange for their used ones. Data from a 99-city study showed that HIV. rates among injecting users in cities with needle exchange programs dropped 19% per year, while cities without needle exchange had an 8% increase per year. (16) Other studies show that HIV infection among drug addicts drops once clean-needle programs are implemented. A study by Don Des Jarlais, a researcher at Beth Israel Hospital in New York, found that HIV rates in New York City dropped more than 75% after city and community activists expanded clean-needle programs in the early 1990s. Despite this evidence, many politicians and policy makers refuse to support these programs. (17) However, data from these aforementioned studies can be used to influence current policy related to needle exchange and demonstrate the need and benefit of these programs.


It is important to use data in public health delivery, and data can be used in many ways and for a variety of critical purposes. Data is crucial to demonstrate and evaluate the impact of an intervention, monitor progress towards a goal, determine barriers to care, and influence public policy.


(1) Kishiki, E., Shirima, S., Lewallen, S., et. al. “Improving postoperative follow-up of children receiving surgery for congenital or developmental cataracts in Africa.” AAPOS. 13.3 (2009).

(2) Kane, M. “Evaluation of the Project to Support PAV (Expanded Program on Immunization) in Northern Mozambique, 2001-2008: An Independent Review for VillageReach With Program and Policy Recommendations.”

(3) Ibid.

(4) Howard, B., Van Horn, L., Hsia, J., “Low-Fat Dietary Pattern and Risk of Cardiovascular Disease.” JAMA. 295. 6 (2006).

(5) “National Cervical Cancer Coalition.” https://www.nccc-online.org/.

(6) Lagrèze, W. “Vision Screening in Preschool Children. Do the Data Support Universal Screening?” Deutsches Arzteblatt International. 107. 28-29. (2010); 495-499.

(7) Belinson, J., Pretorius, R., Zhang, W., et . al. “Cervical Cancer Screening by Simple Visual Inspection After Acetic Acid.” Obstetrics & Gynecology. 98.3 (2001): 441-444.

(8) Ibid.

(9) World Health Organization. “Part 1 Health-related Millennium Development Goals.”

(10) Chandra, A. “Using Health and Family Data from the National Center for Health Statistics to Study Health Disparities.” National Center for Health Statistics. [PowerPoint]. https://sph.unc.edu/mhp/catalog/.

(11) Khalil, I. “Comment on ‘Addressing the Millennium Development Goals From a Surgical Perspective.’” Surgical Care Delivery and World Health. 145.2 (2010): 160.

(12) Weiser, T., et. al. “An Estimation of the Global Volume of Surgery: A Modelling Strategy Based on Available Data.” The Lancet. 372. (2008): 139-44.

(13) Ozgediz, D. “Population Health Metrics for Surgery: Effective Coverage of Surgical Services in Low-Income and Middle-Income Countries.” World Journal of Surgery. 33.1 (2009): 1-5.

(14) Noor, A. M., Mutheu, J. J., Tatem, A. J., Hay, S. I., & Snow, R. W. (2009). Insecticide-treated net coverage in Africa: mapping progress in 2000–07. The Lancet373(9657), 58-67.

(15) Johar, G. V., & Harries, J. (2011). Dial 1298 for ambulance: marketing EMS in Mumbai. Columbia Business School. https://www8.gsb.columbia.edu/caseworks/node/200/Dial%201298%20for%20Ambulance%3A%20Marketing%20EMS%20in%20Mumbai.

(16) Rosenberg, T. “The Needle Nexus.” New York Times Magazine. (2009). https://www.nytimes.com/2009/11/22/magazine/22FOB-idealab-t.html.

(17) Stewart, C. “Why Obama Isn’t Funding Needle-Exchange Programs.” Time. (2009). http://content.time.com/time/nation/article/0,8599,1898073,00.html.