1971 saw Tudor Hart write an enormously influential paper in which he talked about the reasons people face inequalities in gaining access to healthcare. He named this phenomenon the Inverse Care Law (ICL). In his own words:
The availability of good medical care tends to vary inversely with the need for it in the population served. This ICL operates more completely where health care is more exposed to market forces, and less so where such exposure is reduced.
It is a fairly memorable phrase, borrowing terminology from various inverse-square laws in the sciences. However, unlike those inverse-square laws, this law was not based on empirical evidence or rigorously tested experiments. This statement came about through an examination of the common experiences of frontline doctors in the British National Health Service (NHS). Doctors had been talking about these issues for quite a few years at this point. Richard Titmuss (1968) notes that it had been seen that higher income patients "know how to make better use of the service; they tend to receive more specialist attention; occupy more of the beds in better equipped and staffed hospitals; receive more elective surgery, have better maternal care, and are more likely to get psychiatric help and psychotherapy than low-income groups – particularly the unskilled."
Hart argues that this is not an issue one would be able to capture through plain statistics. After all, hospitals make it a point to not note down characteristics such as social class and neighbourhood, and even if they do, getting access to such data tends to be an exercise in frustration. At best, the data one can obtain access to tends to be use-rates across different social groups, that is to say the rates at which different social groups access healthcare services.
However, use-rates can be interpreted in different ways. One of Hart's contemporaries, Rein, writing in 1969, says that the conclusion drawn by Titmuss is abjectly wrong. Since I was unable to access the original paper, I do not have any particular understanding of his argument save what Hart mentions. Rein's argument in the context of Hart's paper is that there are two types of diseases:
- Ones in which low consultation rates are associated with high mortality, which is to say that patients tend die in case they do not get access to a practitioner
- Ones in which high consultation rates are associated with high mortality, which is to say that going to a doctor does not really make any difference
Rein states that these associations remain the same for different diseases regardless of your social class and goes on to say that one cannot make any sense of the argument given by Titmuss. Hart, of course, disagrees. He states that "the more one examines this argument the less it means," and that it can only be used to justify the raison d'être of a universal healthcare system. If there's a disease whose mortality reduces with increased consultation rates then it makes sense to increase consultation rates, and one way of doing that is by increasing everyone's access to doctors.
A modern approach to the ICL
2021 marks the fortieth anniversary of Hart's paper. It has been cited more than 3500 times since its publication and remains a classic in healthcare studies. However, a lot of work has been done since in understanding causes, effects, and factors. The Inverse Care Law has been examined, re-examined, and broken down into its various factors. Most of the data in this section comes from Cookson et al (2021).
Fundamentally, the Inverse Care Law remains the same as when Hart first introduced it. The ICL remains concerned with differences in healthcare which stem from social disadvantage. It doesn't care whether those disadvantages arise from race, class, or ethnicity. If there are two people with the same need for healthcare, the ICL tells us that the more socially advantaged person is more likely to receive it. The ICL cannot tell us anything about the case when two equally socially disadvantaged people have differing needs for healthcare (questions like "will a socially disadvantaged person be more likely to get treatment in case they have cancer or the flu").
However, modern approaches to the ICL differ from Hart's time in terms of certain details. They tend to distinguish between the quantity of healthcare resources (workforce, expenditure per capita, utilisation of services) and inequality in in the quality of care (clinical process and risk-based outcomes). Unlike Hart's paper, most social scientists would define the ICL without alluding to market forces or other causal mechanism. To quote Cookerson et. al.
This definition facilitates a dispassionate scientific approach to investigating different causal mechanisms and how they operate in different social, institutional, and regulatory environments, including the market mechanisms emphasised by Tudor Hart (such as financial barriers for patients and labour market choices by doctors) but also mechanisms that can arise in both market and nonmarket [sic] settings (such as dysfunctional government, nonfinancial [sic] barriers, and unequal costs and benefits of care).
And finally, there are two different types of ICLs identified, both shown in the graph above. The first is the traditional inverse care law, in which increasing social disadvantage leads to dropping standards of healthcare. It can be defined in two parts:
- More socially disadvantaged people tend to have worse health than less socially disadvantaged people
- More socially disadvantaged people tend to receive lesser healthcare and worse healthcare service quality
The second is the disproportionate care law, in which increasing social disadvantage does lead to better healthcare, but not the extent required, causing there to be an increased relative disadvantage compared to the better off. It can also be defined in two parts:
- More socially disadvantaged people tend to have worse health than less socially disadvantaged people
- More socially disadvantaged people tend to receive more healthcare than less socially disadvantaged people, but less as a proportion of need and of generally worse quality
Understanding the concept of "need" and where our processes fall short
It tends to be far easier to measure outcomes and availability as compared to need. Defining need conceptually, harmonising various value judgements, getting detailed data on resource usage, morbidity and other need variables tends to be a herculean task. In general, different variables need to be considered for one to make a good, detailed model of healthcare need. Some of those variables are health behaviours, family support networks, living conditions, travel distance to healthcare facilities, availability of medicines, local labour market conditions, etc. Even when there is agreement about what constitutes a need, the question of whether a need outweighs another whenever there is a resource shortfall tends to be an extremely prickly one. This is often due to clashing and divergent ethical backgrounds of the debaters, but also due to some very fundamental questions about how need ought to be defined: through the lens of cost-effectiveness, through absolute need (as defined by experts), through individual perceptions (the need felt by the population) or through resource use in comparable populations (comparative need).
Keeping this in mind, there are many sources of error. It has been seen that low and middle income countries (LMICs) show a disparity in data availability when it comes to lower and middle income groups. In general, the more socially disadvantaged the population, the more serious tends to be the underreporting about their morbidities.
The experiences of different countries
Various studies have been done to understand the effect of the Inverse Care Law in different countries so as to understand cause and effect better. As a result, it has been found that a complete ICL is seen to operate in most LMICs, where one sees high private expenditure and highly fragmented systems of public funding with extremely high urban-rural divides, and an incomplete ICL (the Disproportionate Care Law) is found to operate in high income countries.
To get a better perspective on this, let us take a look at the experiences of a few different countries.
Brazil is an upper-middle income country with a population of around 220 million people. It is an interesting case to study because of its extremely high GINI coefficient (53.8 in 2018, up from around 51.9 in 2015) and the introduction of the National Programme for Improving Primary Care Access and Quality (PMAQ) in 2011. The data used in this section is taken from Kovacs et. al. (2021).
The PMAQ programme is a pay for performance (P4P) programme launched in order to improve primary care delivery through better allocation of funds and improved organisational arrangements. P4P programmes provide an interesting study in contrasts because of the fundamental question one encounters: will healthcare teams serving richer areas be able to take more advantage of the financial incentives being offered in P4P programmes over teams working in poorer areas?
The data from Brazil are fairly unequivocal about this. The measure used by Kovacs et. al. is a PMAQ score, which takes into account many many variables. It was found that while there was a tendency for teams serving richer areas to have better PMAQ scores, that tendency approached zero as time passed. Income-related inequality of healthcare had been observed in Brazil before the advent of the PMAQ, so to see this inequality reducing seems to indicate that embracing universal healthcare and better, more targeted programmes in which performance is rewarded might be a good way of battling the ICL. One factor in this outcome might have been the fact that teams in poorer areas were given higher performance bonuses as compared to those working in richer areas.
This section uses data from the paper Tangcharoensathien et al (2019). Thailand is considered a fairly typical success story for universal healthcare, with favourable outcomes, including better access to healthcare services, low levels of unmet needs, and low probability of catastrophic health expenditure. Thailand has a predominantly public healthcare system, with nearly 80% of all beds being in government facilities.
The most important part of making a public health programme a success is to make sure that it's planned properly from the outset, because changing direction once it has been set is extremely difficult. Thailand made a good choice in prioritising more than just service delivery: the government decided to recruit more medical students from rural areas, provide hometown placement for doctors, mandate a three-year service in district hospitals for all public school graduates, as well as impose penalties for non-adherence.
In addition, Thailand managed to unify a series of patchwork insurance schemes dating back to the 1970s into a set of three nationwide schemes in 2002. This managed to increase financial efficiency as well as reduce administrative overhead. While integrating these three schemes into one would have been even better, this structure was adopted in order to appeal to the political divisions of the time. The government coupled this with budget reforms which saw the inclusion of more stakeholders and increased transparency in accounting.
Thailand's experiences have seen them flatten the ICL graph. While it is not completely flat, by all accounts, Thailand's health system is remarkably equitable and covers everyone fairly equally.
United States of America
The USA is an outlier among developed countries in that it does not have a universal healthcare system, nor a universal government insurance scheme. Thus, one sees greater differences here as compared to countries with universal healthcare. The data for this section was obtained from the National Healthcare Quality and Disparities Report 2019.
In general it has been reported that more than half the measures used for measuring access showed improvement from 2000 - 2019. Specifically, there was an improvement in people getting access to insurance coverage. Unfortunately, 25% of the measures reported upon showed no improvement and 20% showed worsening. It was also noted that significant disparities persist and some of them have worsened, especially for poor and uninsured populations. Specifically, it was seen that socially disadvantaged minorities received worse quality of care as compared to Whites according to 40% of the quality measures recorded. In contrast, while Asians received worse care going by 30% of the recorded quality metrics, another 30% showed them getting better care than Whites.
It was also noted that America has a fairly large urban-rural divide when it comes to quality of healthcare. A quarter of quality measures showed there being a significant urban/rural divide.
America, being a rich, free country with comparatively few regulations is worse than Thailand in terms of healthcare inequality. The complete ICL is not seen here, but one does observe the effects of a disproportionate care law.
Switzerland is a high-income country with a relatively strange healthcare system as compared to most other countries in its income bracket. Despite the fact that nearly 65% of healthcare is privately funded, healthcare is fairly equitable and affordable to all. The data in this section is primarily taken from the Commonwealth Fund website.
The Swiss government introduced a Health Insurance Law in 1994 which went into effect in 1996. The 1994 law had three basic aims:
- Strengthening equality through universal coverage and subsidies for low-income groups
- Expanding the benefit basket in order to cover more and more conditions and ensure a high standard of healthcare
- Controlling the growing costs of healthcare
In order to do this, citizens are legally required to buy health insurance. The government's role in this system may be summarised as follows:
Duties and responsibilities in the Swiss health care system are divided among the federal, cantonal, and municipal governments. Each of the 26 cantons has its own constitution and is responsible for licensing providers, coordinating hospital services, promoting health through disease prevention, and subsidising institutions and individual premiums. The federal government regulates system financing, ensures the quality and safety of pharmaceuticals and medical devices, oversees public health initiatives, and promotes research and training. The municipalities are responsible mainly for organising and providing long-term care (nursing home care and home care services) and other social support services for vulnerable groups.
Mandatory health insurance is provided by 56 different insurers, all of which are legally required to be non-profit entities. Individuals select an insurer and pay their premiums directly through the companies. The money is then redistributed through a central fund back to the insurers in accordance with a "risk-equalisation scheme that is adjusted for canton, age, gender, and major expenditures in the previous year, such as hospital or nursing home stays and pharmaceutical costs."
Switzerland's unique system differs from what most developed countries offer. However, it has been remarkably successful at flattening the ICL curve.
India represents another extreme in this scheme. It is a middle-income country with almost no universal health coverage to speak of. This led to nearly 85% of all payments to be out of pocket. The data in this section are mainly sourced from Dwiwedi and Pradhan (2017) and Bowser et. al. (2019).
It has been found that the more the per capita out of pocket healthcare spending in a state, the greater the ratio between the spending of the rich and the poor. While this is a fairly obvious result, it was found that higher healthcare spending is concentrated among people residing in urban areas, non scheduled caste and scheduled tribe people, as well as non-Muslims. It was also found that healthcare spending is generally low in certain geographically isolated states: the north eastern states as well as Orissa and Chhattisgarh.
Looking at government-provided services, it was seen that their utilisation tends to be pro-poor. Unfortunately, when net benefits are taken into account, services tend to become more equal and less pro-poor. While the process of reducing inequities in outpatient services has seen progress, in-patient services, which account for a majority of out of pocket spending, have not seen anything resembling advancement. However, there is considerable variation across Indian states which national results tend to hide.
Regardless, the introduction of the National Health Mission and associated programmes has led to significant changes in pro-poor utilisation of services. Unfortunately, India remains a textbook example of the Inverse Care Law. To quote Bowser et. al.:
Although we see a more pro-poor trend in utilisation for deliveries at the national level, when net benefits are included in the analysis, it becomes pro-rich. A potential cause of this trend is that, relative to the unit cost of services, poorer women are paying more OOP [Out of Pocket] for the location where they decide to deliver. This is counterintuitive, as poorer women should theoretically qualify for the different incentive and reimbursement programs that are part of the NHM [National Health Mission]. Randive et al. postulate that these incentives are either insufficient, or that there are other factors accounting for some of this inequality, such as the higher male illiteracy rates or low-quality public health facilities in poorer areas. A qualitative study by Vellakkal et al. also highlights several impediments to institutional delivery. They note that, due to other associated costs (e.g. informal payments), the cash incentive component of JSY [Janani Suraksha Yojana, a component of the National Health Mission] is not an enabling factor for institutional delivery in health facilities, and may actually cause poorer groups to opt out of utilising such initiatives.
Causes of the ICL and healthcare inequality
Judging from these five examples, it is difficult to find causal factors for anything relating to inequalities in healthcare. However, one can hypothesise about a few proximate and distal causes.
- The first, and perhaps most important cause tends to be financial barriers: if one cannot afford it, one cannot get it. This tends to mostly be a problem in countries where a majority of healthcare spending is out-of-pocket. India is a textbook example. A country which bucks this trend is Switzerland, where, despite most healthcare spending being private, benefits are still distributed very equitably
- A second important cause tends to be the fragmentation of insurance due to differential eligibility. This difference in eligibility generally occurs because service providers in more socially advantaged communities have greater access to both political and economic power. The United States provides an obvious example of this issue. Another country highlighted by Cookerson et. al. falls on the opposite end of the spectrum: Mozambique. Most healthcare spending is financed by external donors, which tends to be concentrated in easier to reach urban areas. On the other hand, Brazil has managed to reduce this disparity through its P4P healthcare programme
- A third issue is non-financial barriers. These may be education or literacy, fewer disabilities, better social support systems from family and friends, cultural homogeneity with the doctor and other treating staff, etc. India and the United States provide some examples of this issue. In the United States, Blacks, Native Americans and Hispanics tend to have worse outcomes as compared to Whites and Asians. India sees forward castes and non-Muslims receive better care than other social groups
- The fourth issue is an offshoot of the third issue. Socially advantaged people are better placed to follow the recommendations of doctors in terms of taking time off for rest and recovery, following certain diets, changing one's surroundings etc. Socially disadvantaged people generally tend not to have those options
- The fifth problem is the costs and benefits of healthcare delivery. Socially disadvantaged groups tend to have more co-morbidities, more health issues, and more social and psychological problems which leads to more stress on healthcare systems when extended to cover these populations. At this stage, the cost-benefit ratio might not work in favour of whatever entity is providing healthcare coverage
There are other, more systemic factors which affect the provision of healthcare which are endemic to health systems and human nature:
- Doctors wish to work in more affluent areas because there are better facilities available
- The doctors who wish to work in less affluent areas usually wish to do so because of personal or family ties to the area
- There is an over-representation of professional families in the ranks of doctors (who are usually socially advantaged and urban)
- There is a lot of work pressure in socially disadvantaged areas because socially disadvantaged populations tend to have greater burden of disease
- Healthcare facilities (labs, equipment, etc.) tend to be easily available and more plentiful in socially advantaged areas because it is more cost-effective to have them there
And finally, there are more distal factors which affect inequality of healthcare provision among different social groups, leading to a stronger ICL for that country:
- Poor governance
- Low healthcare spending
- Increased wealth inequality
- Being a low or middle income country
- Political power being unevenly distributed
What can be done about it?
There isn't a straightforward answer to this question. The unique combination of factors present in every country guarantees that a single method or framework will not work for everyone. Brazil went for a P4P scheme, Thailand consolidated its various government insurance programmes into three broad insurers, Switzerland regulated insurance providers and made it illegal for a citizen to not have insurance, the United Kingdom has the NHS, and many other countries have other, different mechanisms to chase the same eventual goal.
Unfortunately, despite all the issues which these systems have managed to solve, there remains the problem of social inequality (different social groups not having the same amount of social capital). Implementing a policy which deals with this problem is fraught with challenges. In fact, trying to solve for this problem might even increase intervention-generated inequalities.
Universal healthcare was seen as the first step towards this goal, and it remains the only step which has some degree of consensus. However, there are many countries which do not wish to go down this route (most notably, the United States) because of the substantial public costs involved, and some countries which have managed to achieve it without increasing public costs at all (Switzerland). Unfortunately, even after having achieved universal healthcare, there remain substantial political and human factors which are difficult to work around. It would be considered ethically wrong to create a policy which restricts the right of movement of a doctor in order to achieve significant health equality. It would also be extremely difficult to convince existing actors in the health system of a country to shelve their interests and work towards a common good.
Despite all these factors it is well-understood that putting money into solving this problem is one of the most important things a society can do in order to reduce wider inequalities in health. A reduction in these inequalities can lead to a positive cascade – investments in primary care, community care, preventive care, and basic surgery tend to have some remarkably far-reaching effects.
The first step to be taken is increasing transparency and creating actionable information where there is none. As Cookson et. al. state very elegantly, "Statistics are the eyes of the state, but the state has a blinkered view when it comes to health inequality impacts. Public decision making still prioritises effectiveness and efficiency over equity, relying on analytical approaches that measure averages rather than social distributions." It is important for healthcare analysis to be very close to the ground: to create forecasts based on costs which actually correspond to ground realities. It is imperative that value-for-money calculations be done without inflating actual spending thresholds in real households.
The second step needs to be the inclusion of data scientists and technology in the arena of healthcare planning. The Apple Watch is the first platform seeing widespread adoption where people are volunteering health data for large-scale studies. These studies need to be extended, made more inclusive (which might be an easier problem than increasing the inclusiveness of healthcare itself) and used to seriously design healthcare programmes and policies. Increasing healthcare costs are inevitable, but gathering this kind of data might be the best way of limiting it as far as possible by eliminating waste and increasing targeting efficiencies.
I wish I could think of a third step. The truth is that COVID-19 has shown us the power of the global healthcare system. When pointed at a problem, it definitely does have the capacity to solve it. Advances in technology might lead to a reduction in healthcare costs which has the potential to reduce financial barriers to healthcare access. Or maybe advances in artificial intelligence will make all these questions and calculations moot.