"Health Inequalities" is a burgeoning field of research that has given rise to many questions and debates about definitions of concepts, analytical strategies, interpretation of findings, and explanatory models.[1] The World Health Organization defines Health inequalities as 'the differences in health status or in the distribution of health determinants between different population groups'. These determinants according to W.H.O are "the conditions in which people are born, grow, live, work and age, including the health system".
According to Webster's dictionary
"Data is factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation."
Data is mainly of two types i.e. Qualitative and Quantitative.
Qualitative data is the type of data that is not given numerically. Hence it's based on people's opinion and choices.
Quantitative data on the other hand is strictly based on numerical values and is subdivided into Discrete (specific numerical values) and a Continuous (any numerical value) data.
Data & Health Inequalities:
The use of data records for health of population is nothing new; the earliest study of a total population was done by Halley, who, by using data for the city of Breslau, Germany, for 1687 to 1691, calculated the average life expectancy at birth.[2] However it was Aaron Antonovsky, a medical sociologist, who first shed light on the inequality in mortality rates in 1967, which ultimately lead to the use of data to record health inequalities Surveillance of inequalities now is done extensively to monitor change and to measure the indicators of health inequalities among the different strata of any region. With every passing year the use of data to monitor and curb health inequalities has become more and more important.
According to the 2007 declaration of The Measurement and Evidence Knowledge Network (MEKN) of the WHO commission on social determinants of health,
"Action on the social determinants of health to improve overall health outcomes and reduce health inequities will be much more effective if basic data systems are in place, nationally and internationally, and there are mechanisms to ensure that the data can be understood and applied to develop more effective interventions." [3]
The point to highlight in this declaration is that data should be understood correctly and applied affectively for it to cause effective interventions. So the question is does all this collected data can be the prime reason for change and reducing health inequalities? The simple answer would be that raw data itself cannot bring any change but the decisions that are taken after processing that data are the main agents of change.
Management of Data:
Data in itself is just a collection of raw numbers or characters. The data collected has to be converted into viable information in order for it to be useful. Here the question arises what the difference is between data and information? Beynon-Davies used the concept of a sign to distinguish between data and information. Data are symbols while information occurs when symbols are used to refer to something. [4] It is people and computers who collect data and impose patterns on it. These patterns are seen as information which can used to enhance knowledge. [5] Thus knowledge is the collection of information that is stored or memorized with the intention of making it useful.
For any knowledge to become useful it must be analyzed and interpreted. The process of understanding the knowledge that we have and using it to synthesize new knowledge is called 'understanding'. The understanding is converted into wisdom when we exercise our innate human nature of morality and ethics. Thus with the help of understanding and the ability to judge right from wrong the data is finally converted into wisdom.
This conversion of raw data into wisdom is called the Data-Information-Knowledge-Wisdom hierarchy. (Fig 1 Appendix)
Another method by which data is managed is the simple data surveillance cycle (Fig 2 Appendix). In this cycle the collected data is analyzed and synthesized and is then organized and stored into a data base. This database then aids policy makers to act as they deem appropriate.
By the study of data management it shows that the decision made at the end of the process is what determines how effectively data has been used and whether data has been successful in bringing about a change. This decision making process can be influenced by ambiguity, bias, desire for short-cuts, resources available and deficit of attention. Any disruption during the data processing cycle can also have negative effects on the decision making.
Data itself has many limitations. It depends upon accuracy; if data is not accurate it can damage a project instead of aiding it. Data should also be complete in all regards i.e. it should provide all expected attributes. The consistency of data is also mandatory i.e. data should be in sync across the enterprise; sometimes data is complete but is inaccurate and inconsistent. Data should also be auditable i.e. it can be traced back to its origin and can be verified for authenticity. Lastly, the most important aspect of data quality is it timeliness. Data should be fresh and up to date so the measures and actions taken in response to the data are appropriate.
Examples of Data Surveillance in Curbing Health Inequalities:
If we take the example of a developing 3rd world country like Pakistan, then it seems as if data cannot help reduce or change anything. Here the question arises that why should we even take the example of a developing country that is bound to fail in curbing inequalities? The reason for this is that Pakistan, even though being a poor country, gets ample funds for its healthcare projects by donor organisations and other rich countries. The international organisations such as WHO closely monitor the statistics of change in the health of the population of the country. So, with copious funds and assistance from international organisations the result should show an improvement in the health of the population, however in Pakistan's case where limited success has been achieved there has also been failure, which proves that the regular surveillance statistics of WHO are not enough for achieving success. This failure of surveillance cannot be blamed entirely upon the collected statistics but there is a complex procedure associated with it that plays an important role in helping the data be effective.
The Polio Eradication Campaign:
In Pakistan the biggest example of the success of data and then its subsequent failure is the national polio eradication drive. Launched in 1994, 15 years after the global drive against polio, the polio campaign started with an aim to fully eradicate the disease by the new century. Even after the century arrived and a decade rolled by, Pakistan has been unable to fulfill its promise of full eradication of the disease by 2010. The campaign suffered from the numerous changes in government over time. When it was launched, the campaign was fueled by a media blitz of awareness programs and ads on TV and in newspapers. This caused the poor, uneducated masses to start accepting and trusting the government to allow them to vaccinate their children. The number of confirmed cases of poliomyelitis based on acute flaccid paralysis surveillance data from across the country declined from 1155 cases in 1997 to 28 in 2005 [6] - the lowest ever recorded in one year (Fig 3, Appendix). A very sensitive nationwide reporting system was built up to assure the detection of all remaining polio cases. The system captures all children aged less than 15 years with acute onset flaccid paralysis, and includes subsequent laboratory testing of stool specimens. [6]
The success was short lived as from 2007 the number of cases came to a standstill, but there was an increase in number of reported cases from small districts and provinces where total immunity was achieved (Fig 4, Appendix). In Punjab e.g. there were no reported cases in 2007; however in 2008 more than 8 cases were reported. The biggest setback to the campaign is due to the ongoing war on terror in Pakistan. In 2008, 2009 and as recent as February 2010 the cases reported were all from the Federally Administered Tribal Areas (FATA) where the people are extremely poor and uneducated and the adjacent province of N.W.F.P. Two new cases were reported in the past week both from North West Frontier Province (NWFP), bringing the total number of cases for 2010 to ten [7]. The most recent case had onset of paralysis on 27 February and that too was reported in the N.W.F.P. The extremist elements in these areas have started a smear campaign against the polio drive and have warned the people to steer clear from vaccinating their children. This includes kidnapping of the polio campaign workers and warnings of dire consequences to the people of the region. [8] The recent involvement of the National Database and Registration Authority (NADRA) in Pakistan to vaccinate children against polio has met with great success. The NADRA vans were successful in issuing ID cards to far flung areas and as a result of the success the government entrusted them with the responsibly of supplying vaccines to remote areas. As many as 20,000 children were vaccinated as a result but the NADRA team was still unsuccessful in venturing to the N.W.F.P and FATA regions.
The migration of Afghan nomads into the areas of FATA & N.W.F.P is another reason for failure. Poor sanitation and unclean water supply in the rural parts of the country can also be the reason for the failure, as this causes diarrhoea which in turn reduces the absorption of the vaccine in children. [9] On top of these hurdles the campaign is also marred by corruption including theft of funds and vaccines. [10]
The National AIDS Program:
The national AIDS program in Pakistan is one example of accuracy of data and how any change can be hindered if the data is falsified. Pakistan's Federal Ministry of Health established National AIDS Control Programme (NACP) in 1986-87. The country has received over 2.9 billion rupees as funding. In its early stages, the programme focused on laboratory diagnosis of suspected HIV cases, but progressively it began to shift its focus towards HIV prevention and control interventions. The development of National Strategic Framework-one in 2001 provided strategic vision to the national response and government of Pakistan with support from World Bank launched an enhanced response in the form of Enhanced HIV and AIDS Control Programme. [11] HIV was first reported in Pakistan in 1987 with the help contaminated blood transfusions. [12] The main carriers of the virus were non resident workers mainly working in the gulf region who were deported back to Pakistan in the aftermath of their diagnosis. [13]
The full fledged outbreak however occurred in 2004 among the injection drug users (IDUs) in remote desert town of Larkana. Between 2003 and 2004 the rate of HIV in IDUs jumped from 0.4% to an alarming 7.6%. Epidemiological studies have shown that out of the 100, 00 IDUs living on the streets, approximately 21% are infected with HIV virus. [11] These IDUs do not indulge in the drugs just for the thrill but instead comprise of the extremely poor and affluent section of the society who are jobless and as a result become drug users to escape from their troubles. Lahore is the 2nd most populous city in Pakistan with over 3000 IDUs of which 4% have HIV infection. Faisalabad is the 3rd most populous city with over 8000 IDUs of which 13% have HIV. The most alarming fact is that studies have found that almost 50% these IDUs are sexually active with their wives. To make matters even worse majority of the wives of IDUs work as prostitutes. [14]
Another demographic involved with the spread of HIV are the female and male sex workers. There are well known brothels in the metropolitan cities of the country with up to 100, 000 female sex workers (FSW) in Karachi and 75, 000 in Lahore. The government bodies estimate that HIV prevalence among FSW is 0.02% [11] which is disputed by independent bodies who say that it is about 15%. Research has shown that these FSW have no information about condoms or other prevention methods. Less than half the FSWs in Lahore and about a quarter in Karachi had used condom with their last regular client. In Karachi, one in five sex workers cannot recognize a condom, and three-quarters do not know that condoms prevent HIV (in fact, one third have never heard of AIDS. (UNIADS Update 2005) The other demographics for the disease include closeted homosexual men and Transvestites moonlighting as sex workers which are estimated to be about 30% of the mode of transmission.
Breast feeding mothers are another demographic that constitutes about 3% of the mode of transmission of the virus. The fist case of transfer of HIV via breast feeding was reported in 1994 in Rawalpindi.
Irrespective of all this data the most shocking fact is the falsification and manipulation of the data by the National AIDS control program who have estimated that there are about 3,000 cases of HIV in Pakistan since 1986. If we compare these estimates to the staggering 70-80, 000 cases reported by the UNAIDS, we clearly see that the government initiatives are a facade. This estimate is shocking enough to result in immediate action by the government but that is not the case. In reality the government is least interested in addressing the issue of AIDS as a reality in Pakistan. This may be due to the fact that the country is a conservative Muslim country and even now issues like HIV & AIDS are considered as taboo. In the uneducated and poor rural sector the disease is still considered as a stigma even though there unsafe and closeted sex practice in these areas. The political decision making in this issue seems to be influenced by the delicateness of the issue. The policy makers are also bound by the culture of the country and they cannot openly advertise safe sex practices as this may be unacceptable to the people and the policy makers do not want to be seen as too liberal by the conservative voting population.
In the light of all this the World Bank in December of 2009 refused to further fund the AIDS program in the country.
Conclusion:
The above examples show that data itself can only do so much. Astonishing and astounding figures may drive someone to think about the problem and take some kind of action but the magnitude of that action is dependent upon the understanding, interpretation and ultimately decision of that person. In the case of the polio awareness campaign the above example shows that data can be used to an advantage in making public alter their beliefs and accepting change. But on the other hand data can be useless, even if it is hard hitting, if no action is taken upon it i.e. in the case of the AIDS campaign. If the Pakistani government wants it can change the attitude of people towards safe sex practice via an awareness campaign but they choose to remain mute on the issue. Policy makers can use data to their advantage by using the figures and getting the public to think about the problem. Similarly the public can use data to demand a change from the government. Not only decision making but other social factors also stand in the way of a complete or any success. Supposedly, If the example of a third world country is deemed inadequate by someone then we can always take the example of Britain where there are advance methods of data surveillance and a huge amount of research and money is being used to curb health inequalities but a recent government report showed that the rates of indicators like life expectancy for women and infant mortality are still unchanged. This has prompted the health minster Dawn Primarolo to finally admit, in lieu with our argument, that "health inequalities are difficult to change".
Appendix:
Fig 1: DIKW Hierarchy
Fig 2: Surveillance Cycle
Fig 3: Graph 1 (decline of polio over the years)
Fig 4: Graph 2 (decline and resurgence of polio in small districts of Pakistan)
