EVALUATING THE QUALITY AND USEFULNESS OF DATA FROM
CURRENT HIV/AIDS SURVEILLANCE SYSTEMs IN USA
Benjamin K Ngugi
Information Systems & Operation
Management Department,
Suffolk University, Boston, MA
[email protected]
Richard G Wamai
Department of African American
Studies, Northeastern University
Boston, MA
[email protected]
ABSTRACT
Purpose: This study evaluates the quality and usefulness of data from the current HIV
Surveillance Systems in USA.
Method: Using the content analysis method, data from the current national HIV surveillance
system is evaluated against a “quality” and “usefulness” criteria developed from previous
literature.
Results: Several shortcomings are identified. These shortcomings weaken the nation’s ability to
track, report and respond to the new HIV epidemiological trends. The problems may also have
led to a misalignment in the way resources are allocated by failing to properly target the key
subpopulations that need the highest resources by virtue of being at the highest risk of HIV
infection. Several recommendations are suggested to address the problems.
INTRODUCTION
Public health surveillance systems play a critical role in national health management (German et
al., 2001; Thacker & Berkelman, 1988) . They provide data that can be used for several
purposes. In particular, the analysis and interpretation of data from HIV/AIDS surveillance
systems plays a critical role in observing emerging trends and making intervention decisions
(Glynn, Lee, & McKenna, 2006; Stoto, 2008). Surveillance data in the US is used “to monitor
the spread of HIV infection, to target HIV prevention programs and health-care services, and to
allocate funding for HIV prevention and care” (Glynn et al., 2006).
HIV/AIDS continues being a key challenge in the public health arena in the US. The current
epidemiological report as well as data reported in the first study using lab technology to make
direct estimates puts the number of people living with HIV/AIDS in the US at 1.2 million
(Center for Disease Control and Prevention, 2010a; Hall et al., 2008). By the end of 2007, over
half a million people had died in America from AIDS (Center for Disease Control, 2007).
Further, HIV/AIDS interventions consume a considerable portion of the nation’s budget. For
example, President Obama’s fiscal year 2011 federal budget request included a $ 20.5 billion for
domestic HIV/AIDS funding (Kaiser Family Foundation, 2010)
Even more worrying is the direction that the prevalence rates are taking. For example, while new
infection rates are declining globally by 19%, and in 22 sub-Saharan Africa countries by 25%,
during 1990-2009 (UNAIDS, 2010), the US diagnosis of new infections has increased by 8%
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during 2005-2008 (Center for Disease Control and Prevention, 2010a). In the District of
Columbia at the end of 2006, at least 3% of residents had HIV or AIDS with the rate reaching
almost 6.5 % among African American men (Department of Health-Washington DC, 2008). This
rate is higher than that of West Africa and at par with the rates in some parts of Uganda and
Kenya (UNAIDS, 2010). The US report indicates that the latest data represents a 22% increase
in HIV and AIDS cases over the previous data period (Department of Health-Washington DC,
2008). These facts suggest a critical need for increased surveillance of HIV/AIDS
epidemiological trends.
Further, it is critical that an evaluation of the quality and usefulness of the data from the
HIV/AIDS surveillance systems be done regularly to ensure that the HIV pandemic is being
monitored effectively and efficiently (Center for Disease Control, 2001). Such evaluations
should also make suggestions on possible improvements, a matter that has been acknowledged in
the current HIV surveillance report (Center for Disease Control and Prevention, 2010d). It is also
critical to develop the evaluation criteria to be used to measure HIV surveillance systems.
Researchers and practitioners can use such a metric to constantly evaluate and improve the
current system in response to new trends and demands.
This study evaluates the “quality” and “usefulness” of the current HIV Surveillance Systems in
USA. The following section provides the methodological approach and evaluation criteria for
HIV surveillance systems from previous literature. After that we comment on current HIV
surveillance system’s data in light of the emerging epidemiological trends at the national levels
and the shortcomings identified. Lastly we conclude with a few recommendations.
METHODOLOGY/APPROACH
This study used the following methodology:
Step 1- Review the existing literature and develop a yardstick that can be used to
evaluate the “quality” and “usefulness” of data from CDC HIV surveillance
system as explained in the next subsection.
Step 2 - Apply the developed yardstick using the content analysis method to
evaluate data from the HIV/AIDS surveillance systems in U.S. Content analysis is
a "technique for making inferences by objectively and systematically identifying
specified characteristics of messages” (Holsti, 1969). The communications in this
study were mostly the HIV surveillance reports on diagnosed HIV infections and
AIDS in the USA and dependent areas that are periodically issued by CDC. For
example, see the following reports (Center for Disease Control and Prevention,
2008b, 2009, 2010a).
Step 3- Identify the successes and shortcomings of the national HIV surveillance
data from the above evaluation and make suggestions on how the shortcomings
can be mitigated.
Our evaluation and study focus was led by the following two research questions-:
RQ1- What is the quality of the information from the HIV/AIDs surveillance systems?
RQ2: What is the usefulness of the information from the HIV/AIDs surveillance systems?
Creating the Yardstick for Evaluating the HIV Surveillance System
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The purpose of evaluating public heath surveillance systems is to ensure that problems of health
importance are being monitored efficiently and effectively (German et al., 2001). These
researchers recommend regular evaluation of the quality and usefulness of data from public
health surveillance system.
Sixteen metrics have been previously suggested for measuring the quality of data by researchers
in the data quality field (Wang & Strong, 1996). The most relevant among these for
HIV/Surveillance systems were found to be: 1) representativeness, 2) completeness, and 3)
accuracy.
Representativeness measures the extent to which the surveillance systems data is a true
reflection of the general population distribution (German et al., 2001). For example,
representativeness can measure how well the different demographics are represented by the HIV
surveillance system’s data.
Completeness refers to the “extent to which data is of sufficient breadth, depth, and scope for the
task at hand” (Wang & Strong, 1996). The extent to which an organization can provide
outstanding services is a function of its ability to achieve the appropriate level of data
completeness and in the process fully understand the consumer needs (Brohman, Watson,
Piccoli, & Parasurama, 2003).
Accuracy refers to “the extent to which data is correct, reliable, and certified free of error”
(Wang & Strong, 1996). Data accuracy is the foundation dimension of data quality because all
the other dimensions matters little if the data is not accurate (Olson, 2003).
A central tenet in data quality literature is that the data must not only be of high quality
but it must also be useful. The “fit to use” principle in data quality suggest that beauty is in the
eyes of the beholder hence data is only of highly quality if it is useful to the consumer (Juran &
Godfrey, 1999). Indeed, the concept of usefulness is built into the very definition of data quality
in Wang and Strong’s (1996, p. 6) model in which they define data quality as data that is fit for
use by data consumers. This concept has been adopted into the HIV surveillance field where
researchers have proposed a guideline for evaluating HIV surveillance systems data with
“usefulness” being a central determinant (German et al., 2001).
Thus the second part of the yardstick development was to identity items that would help
measure the usefulness of HIV surveillance systems data. Several metrics have been suggested:
Previous literature suggest that data generated by HIV surveillance systems can be used in three
major ways (Pisani, Lazzari, Walker, & Schwartländer, 2003): First, the data can be used to plan
and design for the right HIV interventions and programming decisions by tracking the recent
trends, affected geographic areas and the key populations sections that have been adversely
affected by HIV/AIDS as well as better understanding of the affected population behavioral
patterns. A second and important usage of HIV/AIDS surveillance data is in monitoring and
evaluating the success of the national response in the fight against HIV. Such data should be
combined with behavioral data to make sure the right interpretations are being made. Third, HIV
surveillance systems data can be used to expand awareness and to lobby for increased resources
in funding, staffing and the response effort towards the HIV epidemic at the local, national and
international level. This can be done from different standpoints like economic, social and
political.
Current US HIV/AIDS surveillance data are based on estimates (Hall et al., 2008).
German et al (2001) suggest that data from HIV surveillance system should be useful in
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preventing and controlling an outbreak; in revealing a deeper understanding of the implications
of the disease and in providing performance measures and indicators that will form the basis of a
needs assessment. CDC suggests that the data from public surveillance system should help in
detecting changes in the burden of the disease on the society (Center for Disease Control, 2001).
The data should also help in identifying and focusing resources in the subpopulations at the
highest risk of infection (UNAIDS & World Health Organization, 2010)
RESULTS AND DISCUSSION: EVALUATING THE NATIONAL HIV/AIDS
SURVEILLANCE SYSTEM’s data
Brief Description of CDC HIV/AIDS Surveillance System
Figure 1: How the National HIV/AIDS Surveillance System Works (Center for Disease Control
and Prevention, 2000)
4
Comprehensive HIV surveillance guidelines were issued in 1999 (Center for Disease Control and
Prevention, 1999). According to the established norms, CDC employs both passive and active
strategies in collecting national HIV/AIDS surveillance data as shown in Figure 1. In the passive
strategy, an individual will go to health provider in a local practice, hospitals or clinics and have
a HIV test taken. The related laboratory sends a negative or positive test result to the ordering
health provider. HIV is a reportable disease hence both the laboratory and the ordering health
provider will have to report any positive HIV case to the local health department. In addition,
the local health department surveillance personnel actively solicit for information by contacting
health care practitioners and reviewing medical records in hospitals and clinics (Center for
Disease Control and Prevention, 2000). The local health department then reports this information
to the state health department who in turn transmit the information to the national center for
disease control after removing the duplicates.
Earlier, we identified two key metrics for assessing the value of data from HIV surveillance
systems: The first was the quality of data from the surveillance systems which for this study will
look at the data representativeness, completeness and accuracy. The second one is in the
usefulness of the data surveillance systems. In the following, we discuss these in two areas,
namely quality and usefulness of HIV surveillance systems.
Usefulness of Data from the National HIV Surveillance System
The data from the current CDC system has played a crucial role in helping the government
monitor, control and publicizes the HIV pandemic. Several achievements have been made: The
first measure concerns integrating different data sources. CDC working with state and other
government agencies planned for a National Electronic Disease Surveillance System (NEDSS).
The primary goal of the system was to connect different states surveillance systems, expand, and
allow the government to respond more quickly to public health threats (Center for Disease
Control, 2001). The base module is already working allowing states to manage more than 140
diseases and conditions (Center for Disease Control and Prevention, 2010c). This will go a long
way in addressing the lack of integration among the different systems.
The second measure involves creation of HIV/AIDS definition guideline. Qualifying
cases must be diagnosed with the one or more of the "AIDS indicators" diseases or be HIVinfected with a CD4 T-lymphocyte count of less than 200 cells/ml as defined by the CDC
guidelines (Center for Disease Control and Prevention, 2010a). This creates a uniform baseline
for determining the qualifying case for all the physicians.
Third, CDC has moved to HIV based reporting (Center for Disease Control and Prevention,
2010d) rather than reporting based on AIDS infections cases as done since previous guidelines
(Center for Disease Control and Prevention, 1999). The use of antiretroviral medicines has
slowed down the progression of HIV to AIDS meaning AIDS based reporting can no longer be
relied upon to give updated movements in the disease trends which is required for timely
response. Thus HIV based reporting gives a more complete picture of the HIV/AIDS epidemic
and can also help predict the expected level of AIDS in the future based on the average
progression rates.
Fourth, as of April, 2008, CDC requires that all the fifty states and the five dependent areas
report the AIDS/HIV cases by individual names which are kept confidential by CDC. This gives
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higher accuracy and can be easily compared with other related infections which are reported by
names.
Fifth, since January 2008, CDC is encouraging and funding states to use the latest technology to
estimate the infection time for all the HIV cases being detected. This is because a new HIV
diagnosis incident does not necessarily mean that the infection is recent. The person could have
been with HIV for several years undetected. A significant number of people with HIV in the US
have never been tested. Indeed, a previous study noted that there are 25% of people with
undiagnosed HIV who are responsible for 54-70% of new infections (Marks, Crepaz, & Janssen,
2006)
The recommended method developed by CDC is called Serologic Testing Algorithm for Recent
HIV Seroconversion (Janssen et al., 1998). This method uses two enzymes immunoassays
(EIAs) with different sensitivities to the levels of HIV antibodies. A specimen reaction to the
standard EIA but not to the less sensitive EIA, suggest a new infection (Center for Disease
Control and Prevention, 2005). This measure will give more accurate infection data rates and
hence lead to better evaluation of the decrease or increase of year by year new infections. In
summary, data from the HIV/AIDS surveillance systems have helped meet several milestones.
HIV Surveillance System Data Quality Challenges
There are several shortcomings with the quality of data from the current HIV reporting and
surveillance systems being used by CDC. The first shortcoming is that the data is not
representative of the general population. The data only include the cases of those individuals
who have been tested (Center for Disease Control and Prevention, 2000). This means that the
data does not include those cases of people with HIV who have not been tested. There are several
reasons why people may not have gone for testing. It could simply be that the individual is
healthy and has not had a reason for being tested. Other reasons include lack of information
about availability of testing, cost of testing, and confidentiality of results, stigma and
misconceptions (Kaiser Family Foundation, 2010). The health provider may also not have
recommended a test. For example in Washington DC while 79.7% in a 2006-7 survey said they
had seen a healthcare provider, only 49.4% were offered a test (Department of HealthWashington DC & George Washington University School of Public Health, 2009). Further,
access to and type of health insurance may also influence HIV testing and services utilization
(Tao, Kassler, & Paterman, 1998).
Despite the fact that CDC now recommend routine testing for all patients age 13 to 64 years
(Center for Disease Control and Prevention, 2006), nationally only about 53 % of the population
above 18 years reports having been tested (Kaiser Family Foundation, 2010). To make matters
worse, public awareness of HIV is going down. For example, the share of those reporting seeing
advertisement and hearing about the domestic HIV epidemic today has declined to about half
that of five years ago (Kaiser Family Foundation, 2010). This would suggest that there is need
for more awareness campaigns to increase the number of people being tested which studies show
can result in reduced new infections (Pinkerton, Holtgrave, & Galletly, 2008) and is cost
effective (Bartlett et al., 2008; Holtgrave, 2007 ).
The second shortcoming is that the data is neither complete nor accurate. There are several
shortcomings that weaken the completeness and accuracy of the CDC data. CDC advised states
to move from code-based to name-based HIV case reporting in 1999 (Center for Disease Control
and Prevention, 1999). This was later strengthened to a recommendation in 2005 and into a
6
requirement in 2008 (Center for Disease Control and Prevention, 2008c). Name based reporting
was found to be more accurate and can be easily compared with other related infections which
are reported by names. One unintended consequence is that the CDC directive resulted at least in
the short term in incomplete and inaccurate data. We will use the state of California to
demonstrate this point. California was using a code-based reporting method from 1983 but
changed to the name-based system in 2006. However as of April 2009, only about 36,000 cases
of HIV had been reported by local health departments to the state by name despite the fact it was
estimated in 2008 that California has between 68,000 and 106,000 HIV cases (Legislative
Analyst's Office- California, 2010). This suggest that more than half of the HIV cases are yet to
be captured by the name based reporting system. This may result in California getting less state
funding which is based on the number of reported name-based HIV cases ("The Ryan White
HIV/AIDS Treatment Modernization Act of 2006," 2006). Thus the inaccuracy of the system
may skew the allocation of resources and may weaken the nation’s capability of predicting new
disease trends in the state of California.
Starting to report the data using confidential name reporting does not guarantee inclusion of all
the data either. Rather, there is a delay period because CDC requires that states must have been
in the confidential name-based reporting for two to three years before the data is considered
stable enough to be included in the national data reports (Center for Disease Control, 2008). As
of the 2010 report which was the last most updated report, only 37 states and five dependent
areas had been doing confidential name-based reporting long enough to be considered stable as
per this requirement. This means that the data from the remaining 13 states is missing which
adds to the incompleteness of the current national data. This represent approximately 32 % of the
data which is substantial by any count (Center for Disease Control and Prevention, 2010a).
Additionally, CDC expects that the name-based reporting system matures in four years when
trends could be regarded as reliable (Centers for Disease Control & Health Resources and
Services Administration, 2010).
Another issue of inaccuracy may result from adjustments in data reported to the CDC and to the
Health Resources Services Administration (HRSA) based on gaps in code-based and name-based
reporting. HRSA continually monitors the care and treatment of groups and areas funded under
the Ryan White. As the main instrument of federal funding for HIV countrywide, under the 1987
Ryan White Act, living non-AIDS cases reported directly to the Health Resources Services
Administration (HRSA) from the eligible code-based reporting areas are adjusted by 5% to cater
for duplicative reporting ("The Ryan White HIV/AIDS Treatment Modernization Act of 2006,"
2006). The rule is applied unevenly – it is not applied to areas with name-based reporting – and
while controlling funding, may have political effects and affect the tallying of cases.
Emanating from the late 1970s, the US government tracks the progress of the health of
Americans among in several areas including HIV. Current updates show that some of the goals
such as condom use among unmarried sexually active men, testing according to guidelines, HIV
diagnosis prior to AIDS, and HIV/AIDS acquired perinatally, as well as heterosexually
transmitted HIV/AIDS in women could not be assessed because there was no available data
(Centers for Disease Control & Health Resources and Services Administration, 2010).
Resulting Usefulness Challenges
7
The current HIV surveillance system has served the country well as explained earlier. However
the system has not been as usefulness as it potentially can be especially in meeting second
generation HIV surveillance systems goals (World Health Organization & United Nations
Programmes on HIV/AIDs, 2000). The ideal system should concentrate resources where they
will yield the most useful information in helping reduce HIV spread and taking care of those
affected (Committee on the Ryan White CARE Act, 2004; UNAIDS & World Health
Organization, 2010). This means the systems should concentrate its data collection in population
most at risk of becoming newly infected with HIV (World Health Organization & United
Nations Programmes on HIV/AIDs, 2000).
To understand the above shortcoming, it is important to investigate how well the HIV system has
helped in concentrating resources and focus on the subpopulations at the highest risk of
infection. This question cannot be answered without identifying the subpopulations that are at the
highest risk of HIV infection. In 2008, 52 % of the Americans diagnosed with HIV were blacks
/African American despite the fact that they only consist of 12 % of the US population (Center
for Disease Control and Prevention, 2010a). Further, the rate of HIV diagnosis among blacks
increased from 68/100,000 persons to 74/100,000 between 2005 and 2008 which is the largest
increase in rates of HIV diagnoses by race or ethnicity (Center for Disease Control and
Prevention, 2010b). This suggests that blacks/African American is the leading subpopulation at
the highest risk of infection.
What is CDC doing about this? For some three decades, CDC has been working with various
groups on several initiatives to combat HIV especially among African Americans (Sutton M Y et
al., 2009). The doubling by CDC of its investment in 2008 to $70 million to increase HIV testing
among African American and the launching of a $ 10 million “Act against AIDS campaign” with
14 of the nation’s leading African American organizations are two examples of such initiatives
(Center for Disease Control and Prevention, 2009). The above responses are commendable
efforts by the government. However they are a drop in the ocean especially when calculated as a
fraction of the whole CDC prevention budget allocated to states. For example CDC allocated a
total of $515,000,000 for HIV prevention program for the 50 states and dependent areas for the
2008 fiscal year (Center for Disease Control and Prevention, 2008a). This suggests that the data
has not helped as much as it should in focusing resources on the community at the highest risk of
infection, a matter that is the focus of a recent modeling exercise for more efficient resource
allocation (Lasry, Sansom, Hicks, & V., 2011).
The second implication of the shortcoming is that the criterion used by the surveillance systems
to capture data is not specific enough to customize subpopulation specific programs. For
example, CDC uses “black“ to include all people black, as does the recently released National
HIV Strategy (US White House, 2010). However they fail to recognize the diversity of the
8
blacks/African American label and may be missing some important pockets of these
subpopulations. This fact was recently demonstrated by a study done by analyzing HIV data in
states with high African born communities (Kerani et al., 2008). These were California, Georgia,
Massachusetts, Minnesota, and New Jersey and in King County, Washington; New York City;
and the portion of Virginia included in the Washington, DC, metropolitan area. The researchers
found that African born individuals accounted for only 0.6% of the population yet had 3.8 % of
the HIV cases diagnosed in the participating areas. Even more alarming, they consisted up to
50% of the HIV cases diagnosed in blacks in some areas like Minnesota. This suggest that
classifying HIV cases occurring in African-born population as ‘African American’ or ‘black’ can
at times be misleading and could lead to neglecting of this subpopulation in the allocation of
resources.
A related issue arises when considering immigrant data especially in those states with high
immigrant population. For example, immigrants and refugees born outside the US compromise
20% of the total population of Massachusetts residents living with HIV /AIDS yet they only
consist of 12% of Massachusetts general population (Massachusetts Department of Public
Health, 2008). The report further indicates that the infection rate of the non-US born population
rose from 19% in 1999 to 34% in 2007 showing that the infection rate in this section of the
population is rising rapidly. The leading nine countries of origin for the non US population in
descending order are Haiti, Brazil, Dominican Republic, Uganda, Cape Verde Island, Kenya,
Cameroon, Honduras, El Salvador and Ghana. The largest percentage (34%) of the non-US born
population diagnosed with HIV/AIDS within the three year period 2005 to 2007 was from SubSaharan Africa. This suggests that the country of birth should be included in all cases to have a
more effective surveillance system. It is highly likely that the world global HIV prevalence for
the immigrants from these countries follow the immigrants as they come in and settle in their
specific preferred neighborhoods (Takougang & Tidjani, 2009). Hence, studying the respective
countries prevalence rates and behavior would be a key step in understanding and overcoming
the spread of infection among these groups respectively (Yewoubdar, 2000).
Conclusions and Recommendations
This study assessed the data from current HIV surveillance system in the US on the basis of prior
established criteria for evaluating public heath surveillance systems (German et al., 2001; Wang
& Strong, 1996). Data and sources of data are of critical importance in measuring the HIV/AIDS
epidemic (Brookmeyer, 2010) . Here we have assessed the quality of the current US HIV/AIDS
data and the usefulness of the surveillance system. The results suggest that the system has
achieved certain worthy goals. For example, the system has integrated different data sources at
the national and state levels. Further, a standardized HIV case definition, testing and reporting
guideline has been established.
However there are several shortcomings with the current surveillance system primarily due to the
type of methods used to collect the data and the sources of that data. First the data is not
representative of the general population but rather only reflect the population that has undergone
testing. Second, the data is incomplete and hence inaccurate due to exclusion of states that do not
meet certain stability requirement. For example, states need to have done confidential name
reporting for at least three years to be included. Third, the systems use of “catchall labels” like
9
“blacks” without inclusion of country of origin or other unique identifiers has led to
overgeneralization.
These shortcomings affect the quality of the collected data: This in turn reduces the overall data
usefulness. For example, the system has been less useful in helping concentrate resources and
focus on the subpopulations at the highest risk of infection than expected due to its failure to
recognize the diversity of some groups like the blacks/African American subgroup.
In view of the shortcoming in the state-based national data collection and reporting system, we
recommend that certain changes be made to bolster the usefulness of the data. As HIV transitions
into a more heterosexual transmission, more specification in population-based capture and
reporting, not less, will be needed. Reporting by the current race groups does not adequately
capture the increasingly complex transmission risks due to the cultural and ethnic diversity.
Hence, it can be recommended that future strategies should involve not just race-based but also
country-of-birth-based reporting to account for the role of migration in transmission. This
recommendation will help to boost the successful implementation of the National HIV/AIDS
Strategy, whose focus is reducing new infections, improving access to care and reducing
disparities (Yehia & Frank, 2011).
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