The Mortality Gradient

A young man was admitted to a hospital in North KwaZulu-Natal (a province of South Africa) with a stab wound in the upper left quadrant of the abdomen and a falling blood pressure. Only a skeleton staff was on duty, thanks to a public sector strike taking place at the time. Further, only one member of the team was medically qualified, and she was assisted by two medical students on their elective periods. The doctor anaesthetised and intubated the patient, scrubbed, and then started to open the abdomen. A number of things then happened in rapid succession: the blood was found to have turned a blue colour, the oximetry alarm sounded, and the heart stopped. The doctor opened the chest to give internal cardiac compression but to no avail. On opening the chest the problem was identified. Firstly, the knife had penetrated the diaphragm to enter the left lung; a frequent finding with stab wounds to the upper left abdomen. Secondly, the left main bronchus (windpipe) had been intubated. The result was that only the left lung had been ventilated and air had been forced under pressure into the cavity around the lung. This air, forced into the plural space under pressure, had compressed the chest contents, causing the patient’s oxygen levels to plummet.

Mortality rates from surgery for a given condition are roughly twice as high in low- and middle-income countries (LMICs) as in high-income countries. Why is this?

A very large number of recent studies [1-4] have replicated Aneel Bhangu’s classic study in the British Journal of Surgery,[5] confirming the mortality gradient. That the gradient exists is not in real doubt but its causes are. Possible, non exclusive, causes are:

1. The patient arrives in worse condition in LMICs than in high-income countries because they are in worse shape generally, and/or there were large delays in reaching the health care system.
2. Care is worse before surgery, including longer delays within the health care system, and/or post operative care was suboptimal.
3. Intraoperative care was worse, either in terms of anaesthesia (as in the above example – intubation of the left bronchus is a classic error requiring special vigilance in cases where there is a risk that the integrity of the lung has been compromised), and/or surgery itself.

A fashion has broken out to compare death rates for given conditions across high- and low-income countries, and then ascribe observed differences in outcome to differences in healthcare provision, expressed in terms of lives (or even DALYs) lost. Such an approach can work well at the specific level when two conditions are met:

1. A specific condition is examined and this condition has a poor natural history, but an extremely good prognosis given appropriate medical care. Perinatal haemorrhage and eclampsia are good examples.
2. The way in which healthcare can remedy the situation is well defined. Treatment of ruptured spleen or meningitis are good examples.

Absent conditions fulfilling the above criteria, comparisons between high- and low-income countries should motivate serious investigation for causes. Until the cause of the difference is determined, advocacy based only on differences between high- and low-income countries is without intellectual or moral value. Decisions should be based on the best use of restricted resources and simply pointing out north-south differences in outcomes adds no value to determining priorities within LMICs. It is wasteful to advocate resource allocation under scarcity until the payback among different competing causes has been examined. The correct use of measuring differences in outcome over countries should not be to advocate for resources for action. These differences in outcome should motivate a sober search for these causes, and then for cost-effective short- and long-term solutions.

To that end I am leading a cross NIHR initiative into one particular candidate area – access to hospital when care is sought. I also lead the access theme for the NIHR Global Health Research Unit on Global Surgery. One of our tasks is to model the cost-effectiveness of various solutions to overcome the second barrier to access identified above. Our work is hampered by poor data (poor because it is hard to collect) on effects of delay on outcome. Strangely enough, snakebites is the one area where rather good data exist, so we are starting our work in this, otherwise rather narrow, topic. We also plan to study survival rates in UK by measuring distances from local hospitals in conditions such as leaking aortic aneurysms and heart attack.

In the meantime Dr Bruce Biccard of Cape Town, who leads the hospital care work stream in Global Surgery, is turning his attention from differences in outcome to the causes and remedies.

— Richard Lilford, CLAHRC WM Director

References:

1. Abbott TEF, Fowler AJ, Dobbs TD, Harrison EM, Gillies MA, Pearse RM. Frequency of surgical treatment and related hospital procedures in the UK: a national ecological study using hospital episode statistics. Br J Anaesthesia. 2017; 119(2): 249-57.
2. Anderson GA, Ilcisin L, Abesiga L, et al. Surgical volume and postoperative mortality rate at a referral hospital in Western Uganda: Measuring the Lancet Commission on Global Surgery indicators in low-resource settings. Surgery. 2017; 161(6): 1710-9.
3. GlobalSurg Collaborative. Management and Outcomes Following Surgery for Gastrointestinal Typhoid: An International, Prospective, Multicentre Cohort Study. World J Surg. 2018; 42(10): 3179-88.
4. Biccard BM, Madiba TE, Kluyts H-L. Perioperative patient outcomes in the African Surgical Outcomes Study: a 7-day prospective observational cohort study. Lancet. 2018; 391: 1589-98.
5. GlobalSurg Collaborative. Mortality of emergency abdominal surgery in high-, middle- and low-income countries. Br J Surg. 2016; 103(8): 971-88.

Transport to Place of Care

Availability of emergency transport is taken for granted in high-income countries. The debate in such countries relates to such matters as the marginal advantages of helicopters over vehicle ambulances, and what to do when the emergency team arrives at the scene of an accident. But in low- or low-middle-income countries, the situation is very different – in Malawi, for example, there is no pretence that a comprehensive ambulance system exists. The subject of transport does not seem to get attention commensurate with its importance. Researchers love to study the easy stuff – role of particulates in lung disease; prevalence of diabetes in urban vs. rural areas; effectiveness of vaccines. But study selection should not depend solely on tractability – the scientific spotlight should also encompass topics that are more difficult to pin down, but which are critically important. Transport of critically ill patients falls into this category.[1]

Time is of the essence for many conditions. Maternity care is an archetypal example,[2] where delayed treatment in conditions such as placental abruption, eclampsia, ruptured uterus, and obstructed labour can be fatal for mother and child. The same applies to acute infections (most notably meningococcal meningitis) and trauma where time is critical (even if there is no abrupt cut-off following the so called ‘golden hour’).[3] The outcome for many surgical conditions is affected by delay during which, by way of example, an infected viscus may rupture, an incarcerated hernia may become gangrenous, or a patient with a ruptured tubal pregnancy might exsanguinate. However, in many low-income countries less than one patient in fifty has access to an ambulance service.[4] What is to be done?

The subject has been reviewed by Wilson and colleagues in a maternity care context.[5] Their review revealed a number of papers based on qualitative research. They find the theory that one might have anticipated – long delays, lack of infrastructure, and so on. They also make some less intuitive findings. People think that having an emergency vehicle at the ready could bring bad luck, and that it is shameful to expose oneself when experiencing vaginal bleeding.

Quite a lot of work has been done on the use of satellites to develop isochrones based on distances,[6] gradients, and road provision. But working out how long it should take to reach a hospital does not say much about how long it takes in the absence of a service for the transport of acutely sick patients.

We start from the premise that, for the time being at least, a fully-fledged ambulance service is beyond the affordability threshold for many low-income countries. However, we note that many people make it to hospital in an emergency even when no ambulance is available. This finding makes one think of ‘grass-roots’ solutions; finding ways to release the capacity inherent in communities in order to provide more rapid transfers. An interesting finding in Wilson’s paper is that few people, even very poor people, could not find the money for transfer to a place of care in a dire emergency. However, this does not square with work on acutely ill children in Malawi (Nicola Desmond, personal communication), nor work done by CLAHRC WM researchers showing the large effects that user fees have in supressing demand, especially for children, in the Neno province of Malawi.[7] In any event, a grass roots solution should be sought, pending the day when all injured or acutely ill people have access to an ambulance. Possible solutions include community risk-sharing schemes, incentives to promote local enterprises to transport sick people, and automatic credit transfer arrangements to reimburse those who provide emergency transport.

I am leading a work package for the NIHR Global Surgery Unit, based at the University of Birmingham, concerned with access to care. We will describe current practice across purposively sampled countries, work with local people to design a ‘solution’, conduct geographical and cost-benefit analyses, and then work with decision-makers to implement affordable and acceptable improvement programmes. These are likely to involve a system of local risk-sharing (community insurance), IT facilitated transfer of funds, promotion of local transport enterprises, community engagement, and awareness raising. We are very keen to collaborate with others who may be planning work on this important topic.

— Richard Lilford, CLAHRC WM Director

References:

1. United Nations. The Millennium Development Goals Report 2007. New York: United Nations; 2007.
2. Forster G, Simfukew V, Barber C. Use of intermediate mode of transport for patient transport: a literature review contrasted with the findings of Transaid Bicycle Ambulance project in Eastern Zambia. London: Transaid; 2009.
3. Lord JM, Midwinter MJ, Chen Y-F, Belli A, Brohi K, Kovacs EJ, Koenderman L, Kubes P, Lilford RJ. The systemic immune response to trauma: an overview of pathophysiology and treatment. Lancet. 2014; 384(9952): 1455-65.
4. Nyamandi V, Zibengwa E. Mobility and Health. 2007. In: Wilson A, Hillman S, Rosato M, Costello A, Hussein J, MacArthur C, Coomarasamy A. A systematic review and thematic synthesis of qualitative studies on maternal emergency transport in low- and middle-income countries. Int J Gynaecol Obstet. 2013; 122(3): 192-201.
5. Wilson A, Hillman S, Rosato M, Skelton J, Costello A, Hussein J, MacArthur C, Coomarasamy A. A systematic review and thematic synthesis of qualitative studies on maternal emergency transport in low- and middle-income countries. Int J Gynaecol Obstet. 2013; 122(3): 192-201.
6. Frew R, Higgs G, Harding J, Langford M. Investigating geospatial data usability from a health geography perspective using sensitivity analysis: The example of potential accessibility to primary healthcare. J Transp Health 2017 (In Press).
7. Watson SI, Wroe EB, Dunbar EL, Mukherjee J, Squire SB, Nazimera L, Dullie L, Lilford RJ. The impact of user fees on health services utilization and infectious disease diagnoses in Neno District, Malawi: a longitudinal, quasi-experimental study. BMC Health Serv Res. 2016; 16(1): 595.

Improving Access to Fresh Food in Low-Income Areas

In a previous News Blog we looked at a paper that found an association between adherence to the Mediterranean diet (i.e. high consumption of fruit, vegetables, and legumes) and reduction in cardiovascular disease risk.[1] So, it can be argued, that for those in low-income areas there is a need to improve their access to fresh fruit and vegetables. But how best to achieve this? Breck and colleagues, on behalf of the CDC, looked at one possibility in a cross-sectional survey analysis.[2]

Previously, the city of New York had attempted to address the issue by granting new licenses for mobile fruit and vegetable carts in those neighbourhoods with poor availability of fresh food. However, only some of the carts (27%) had the capacity to accept the Supplemental Nutrition Assistance Program (SNAP) benefits (a federal aid program to provide food-purchasing assistance) through use of Electronic Benefit Transfer (EBT) machines.

The authors conducted a survey analysis of 779 adults shopping at four carts in the Bronx neighbourhood of New York over several time periods. After controlling for cofounders, they found that those shoppers who were able to pay using their SNAP benefits purchased significantly (p<0.001) more fruit and vegetables (an average of 5.4 more cup equivalents), than those who were only able to pay with cash. While there are promising results from providing consumers with more ways to pay, there are challenges that could prevent widespread roll out of EBT, chiefly the high initial, monthly, and transaction fees that the cart vendors need to pay. Even when provided with financial support, less than one-third of carts were equipped with EBT machines at the time of this study. Although the study has a number of limitations that means causal inferences cannot be drawn, it can be seen as a possible avenue for future research.

— Peter Chilton, Research Fellow

Reference:

1. Chilton P. Diet and Socioeconomic Status. 18 August 2017.
2. Breck A, Kiszko K, Martinez O, Abrams C, Elbel B. Could EBT Machines Increase Fruit and Vegetable Purchases at New York City Green Carts? Prev Chronic Dis. 2017; 170104.

Measuring the Quality of Health Care in Low-Income Settings

Measuring the quality of health care in High-Income Countries (HIC) is deceptively difficult, as shown by work carried out by many research groups, including CLAHRC WM.[1-5] However, a large amount of information is collected routinely by health care facilities in HICs. This data includes outcome data, such as Standardised Mortality Rates (SMRs), death rates from ’causes amenable to health care’, readmission rates, morbidity rates (such as pressure damage), and patient satisfaction, along with process data, such as waiting times, prescribing errors, and antibiotic use. There is controversy over many of these endpoints, and some are much better barometers of safety than others. While incident reporting systems provide a very poor basis for epidemiological studies (that is not their purpose), case-note review provides arguably the best and most widely used method for formal study of care quality – at least in hospitals.[3] [6] [7] Measuring safety in primary care is inhibited by the less comprehensive case-notes found in primary care settings as compared to hospital case-notes. Nevertheless, increasing amounts of process information is now available from general practices, particularly in countries (such as the UK) that collect this information routinely in electronic systems. It is possible, for example, to measure rates of statin prescriptions for people with high cardiovascular risk, and anticoagulants for people with ventricular fibrillation, as our CLAHRC has shown.[8] [9] HICs also conduct frequent audits of specific aspects of care – essentially by asking clinicians to fill in detailed pro formas for patients in various categories. For instance, National Audits in the UK have been carried out into all patients experiencing a myocardial infarction.[10] Direct observation of care has been used most often to understand barriers and facilitators to good practice, rather than to measure quality / safety in a quantitative way. However, routine data collection systems provide a measure of patient satisfaction with care – in the UK people who were admitted to hospital are surveyed on a regular basis [11] and general practices are required to arrange for anonymous patient feedback every year.[12] Mystery shoppers (simulated patients) have also been used from time to time, albeit not as a comparative epidemiological tool.[13]

This picture is very different in Low- and Middle-Income Countries (LMIC) and, again, it is yet more difficult to assess quality of out of hospital care than of hospital care.[14] Even in hospitals routine mortality data may not be available, let alone process data. An exception is the network of paediatric centres established in Kenya by Prof Michael English.[15] Occasionally large scale bespoke studies are carried out in LMICs – for example, a recent study in which CLAHRC WM participated, measured 30 day post-operative mortality rates in over 60 hospitals across low-, middle- and high-income countries.[16]

The quality and outcomes of care in community settings in LMICs is a woefully understudied area. We are attempting to correct this ‘dearth’ of information in a study in nine slums spread across four African and Asian countries. One of the largest obstacles to such a study is the very fragmented nature of health care provision in community settings in LMICs – a finding confirmed by a recent Lancet commission.[17] There are no routine data collection systems, and even deaths are not registered routinely. Where to start?

In this blog post I lay out a framework for measurement of quality from largely isolated providers, many of whom are unregulated, in a system where there is no routine system of data and no archive of case-notes. In such a constrained situation I can think of three (non-exclusive) types of study:

1. Direct observation of the facilities where care is provided without actually observing care or its effects. Such observation is limited to some of the basic building blocks of a health care system – what services are present (e.g. number of pharmacies per 1,000 population) and availability (how often the pharmacy is open; how often a doctor / nurse / medical officer is available for consultation in a clinic). Such a ‘mapping’ exercise does not capture all care provided – e.g. it will miss hospital care and municipal / hospital-based outreach care, such as vaccination provided by Community Health Workers. It will also miss any IT based care using apps or online consultations.
2. Direct observation of the care process by external observers. Researchers can observe care from close up, for example during consultations. Such observations can cover the humanity of care (which could be scored) and/or technical quality (which again could be scored against explicit standards and/or in a holistic (implicit) basis).[6] [7] An explicit standard would have to be based mainly on ‘if-then’ rules – e.g. if a patient complained of weight loss, excessive thirst, or recurrent boils, did the clinicians test their urine for sugar; if the patient complained of persistent productive cough and night sweats was a test for TB arranged? Implicit standards suffer from low reliability (high inter-observer variation).[18] Moreover, community providers in LMICs are arguably likely to be resistant to what they might perceive as an intrusive or even threatening form of observation. Those who permitted such scrutiny are unlikely to constitute a random sample. More vicarious observations – say of the length of consultations – would have some value, but might still be seen as intrusive. Provided some providers would permit direct observation, their results may represent an ‘upper bound’ on performance.
3. Quality as assessed through the eyes of the patient / members of the public. Given the limitations of independent observation, the lack of anamnestic records of clinical encounters in the form of case-notes, absence of routine data, and likely limitations on access by independent direct observers, most information may need to be collected from patients themselves, or as we discuss, people masquerading as patients (simulated patients / mystery shoppers). The following types of data collection methods can be considered:
   1. Questions directed at members of the public regarding preventive services. So, households could be asked about vaccinations, surveillance (say for malnutrition), and their knowledge of screening services offered on a routine basis. This is likely to provide a fairly accurate measure of the quality of preventive services (provided the sampling strategy was carefully designed to yield a representative sample). This method could also provide information on advice and care provided through IT resources. This is a situation where some anamnestic data collection would be possible (with the permission of the respondent) since it would be possible to scroll back through the electronic ‘record’.
   2. Opinion surveys / debriefing following consultations. This method offers a viable alternative to observation of consultations and would be less expensive (though still not inexpensive). Information on the kindness / humanity of services could be easily obtained and quantified, along with ease of access to ambulatory and emergency care.[19] Measuring clinical quality would again rely on observations against a gold standard,[20] but given the large number of possible clinical scenarios standardising quality assessment would be tricky. However, a coarse-grained assessment would be possible and, given the low quality levels reported anecdotally, failure to achieve a high degree of standardisation might not vitiate collection of important information. Such a method might provide insights into the relative merits and demerits of traditional vs. modern health care, private vs. public, etc., provided that these differences were large.
   3. Simulated patients offering standardised clinical scenarios. This is arguably the optimal method of technical quality assessment in settings where case-notes are perfunctory or not available. Again, consultations could be scored for humanity of care and clinical/ technical competence, and again explicit and/or implicit standards could be used. However, we do not believe it would be ethical to use this method without obtaining assent from providers. There are some examples of successful use of the methods in LMICs.[21] [22] However, if my premise is accepted that providers must assent to use of simulated patients, then it is necessary to first establish trust between providers and academic teams, and this takes time. Again, there is a high probability that only the better providers will provide assent, in which case observations would likely represent ‘upper bounds’ on quality.

In conclusion, I think that the basic tools of quality assessment, in the current situation where direct observation and/or simulated patients are not acceptable, is a combination of:

1. Direct observation of facilities that exist, along with ease of access to them, and
2. Debriefing of people who have recently used the health facilities, or who might have received preventive services that are not based in these facilities.

We do not think that the above mentioned shortcomings of these methods is a reason to eschew assessment of service quality in community settings (such as slums) in LMICs – after all, one of the most powerful levers to improvement is quantitative evidence of current care quality.[23] [24] The perfect should not be the enemy of the good. Moreover, if the anecdotes I have heard regarding care quality (providers who hand out only three types of pill – red, yellow and blue; doctors and nurses who do not turn up for work; prescription of antibiotics for clearly non-infectious conditions) are even partly true, then these methods would be more than sufficient to document standards and compare them across types of provider and different settings.

— Richard Lilford, CLAHRC WM Director

References:

1. Brown C, Hofer T, Johal A, Thomson R, Nicholl J, Franklin BD, Lilford RJ. An epistemology of patient safety research: a framework for study design and interpretation. Part 1. Conceptualising and developing interventions. Qual Saf Health Care. 2008; 17(3): 158-62.
2. Brown C, Hofer T, Johal A, Thomson R, Nicholl J, Franklin BD, Lilford RJ. An epistemology of patient safety research: a framework for study design and interpretation. Part 2. Study design. Qual Saf Health Care. 2008; 17(3): 163-9.
3. Brown C, Hofer T, Johal A, Thomson R, Nicholl J, Franklin BD, Lilford RJ. An epistemology of patient safety research: a framework for study design and interpretation. Part 3. End points and measurement. Qual Saf Health Care. 2008; 17(3): 170-7.
4. Brown C, Hofer T, Johal A, Thomson R, Nicholl J, Franklin BD, Lilford RJ. An epistemology of patient safety research: a framework for study design and interpretation. Part 4. One size does not fit all. Qual Saf Health Care. 2008; 17(3): 178-81.
5. Brown C, Lilford R. Evaluating service delivery interventions to enhance patient safety. BMJ. 2008; 337: a2764.
6. Benning A, Ghaleb M, Suokas A, Dixon-Woods M, Dawson J, Barber N, et al. Large scale organisational intervention to improve patient safety in four UK hospitals: mixed method evaluation. BMJ. 2011; 342: d195.
7. Benning A, Dixon-Woods M, Nwulu U, Ghaleb M, Dawson J, Barber N, et al. Multiple component patient safety intervention in English hospitals: controlled evaluation of second phase. BMJ. 2011; 342: d199.
8. Finnikin S, Ryan R, Marshall T. Cohort study investigating the relationship between cholesterol, cardiovascular risk score and the prescribing of statins in UK primary care: study protocol. BMJ Open. 2016; 6(11): e013120.
9. Adderley N, Ryan R, Marshall T. The role of contraindications in prescribing anticoagulants to patients with atrial fibrillation: a cross-sectional analysis of primary care data in the UK. Br J Gen Pract. 2017. [ePub].
10. Herrett E, Smeeth L, Walker L, Weston C, on behalf of the MINAP Academic Group. The Myocardial Ischaemia National Audit Project (MINAP). Heart. 2010; 96: 1264-7.
11. Care Quality Commission. Adult inpatient survey 2016. Newcastle-upon-Tyne, UK: Care Quality Commission, 2017.
12. Ipsos MORI. GP Patient Survey. National Report. July 2017 Publication. London: NHS England, 2017.
13. Grant C, Nicholas R, Moore L, Sailsbury C. An observational study comparing quality of care in walk-in centres with general practice and NHS Direct using standardised patients. BMJ. 2002; 324: 1556.
14. Nolte E & McKee M. Measuring and evaluating performance. In: Smith RD & Hanson K (eds). Health Systems in Low- and Middle-Income Countries: An economic and policy perspective. Oxford: Oxford University Press; 2011.
15. Tuti T, Bitok M, Malla L, Paton C, Muinga N, Gathara D, et al. Improving documentation of clinical care within a clinical information network: an essential initial step in efforts to understand and improve care in Kenyan hospitals. BMJ Global Health. 2016; 1(1): e000028.
16. Global Surg Collaborative. Mortality of emergency abdominal surgery in high-, middle- and low-income countries. Br J Surg. 2016; 103(8): 971-88.
17. McPake B, Hanson K. Managing the public-private mix to achieve universal health coverage. Lancet. 2016; 388: 622-30.
18. Lilford R, Edwards A, Girling A, Hofer T, Di Tanna GL, Petty J, Nicholl J. Inter-rater reliability of case-note audit: a systematic review. J Health Serv Res Policy. 2007; 12(3): 173-80.
19. Schoen C, Osborn R, Huynh PT, Doty M, Davis K, Zapert K, Peugh J. Primary Care and Health System Performance: Adults’ Experiences in Five Countries. Health Aff. 2004.
20. Kruk ME & Freedman LP. Assessing health system performance in developing countries: A review of the literature. Health Policy. 2008; 85: 263-76.
21. Smith F. Private local pharmacies in low- and middle-income countries: a review of interventions to enhance their role in public health. Trop Med Int Health. 2009; 14(3): 362-72.
22. Satyanarayana S, Kwan A, Daniels B, Subbaramn R, McDowell A, Bergkvist S, et al. Use of standardised patients to assess antibiotic dispensing for tuberculosis by pharmacies in urban India: a cross-sectional study. Lancet Infect Dis. 2016; 16(11): 1261-8.
23. Kudzma E C. Florence Nightingale and healthcare reform. Nurs Sci Q. 2006; 19(1): 61-4.
24. Donabedian A. The end results of health care: Ernest Codman’s contribution to quality assessment and beyond. Milbank Q. 1989; 67(2): 233-56.

Private Consultations More Effective than Public Provision in Rural India

Doing work across high-income countries (CLAHRC WM) and lower income countries (CLAHRC model for Africa) provides interesting opportunities to compare and contrast. For example, our work on user fees in Malawi [1] mirrors that in high-income countries [2] – in both settings, relatively small increments in out-of-pocket expenses results in a large decrease in demand and does so indiscriminately (the severity of disease among those who access services is not shifted towards more serious cases). However, the effect of private versus public provision of health care is rather more nuanced.

News Blog readers are likely aware of the famous RAND study in the US.[3] People were randomised to receive their health care on a fee-for-service basis (‘privately’) vs. on a block contract basis (as in a public service). The results showed that fee-for-service provision resulted in more services being provided (interpreted as over-servicing), but that patients were more satisfied clients, compared to those experiencing public provision. Clinical quality was no different. In contrast, a study from rural India [4] found that private provision results in markedly improved quality compared to public provision, albeit with a degree of over-servicing.

The Indian study used ‘standardised patients’ (SPs) to measure the quality of care during consultations covering three clinical scenarios – angina, asthma and the parent of a child with dysentery. The care SPs received was scored against an ideal standard. Private providers spent more time/effort collecting the data essential for making a correct diagnosis, and were more likely to give treatment appropriate to the condition. First, they compared private providers with public providers and found that the former spent 30% more time gathering information from the SPs than the public providers. Moreover, the private providers were more likely to be present when the patient turned up for a consultation. There was a positive correlation between the magnitude of fees charged by private providers and time spent eliciting symptoms and signs, and the probability that the correct treatment would be provided. However, the private providers are often not doctors, so this result could reflect different professional mix, at least in part. To address this point, a second study was done whereby the same set of doctors were presented with the same clinical cases – a ‘dual sample’. The results were even starker, with doctors spending twice as long with each patient when seen privately.

Why were these results from rural India so different from the RAND study? The authors suggest that taking a careful history and examination is part of the culture for US doctors, and that they had reach a kind of asymptote, such that context made little difference to this aspect of their behaviour. Put another way, there was little headroom for an incentive system to drive up quality of care. However, in low-income settings where public provision is poorly motivated and regulated, fee-for-service provision drives up quality. The same seems to apply to education, where private provision was found to be of higher quality than public provision in low-income settings – see previous News Blog.[5]

However, it should be acknowledged that none of the available alternatives in rural India were good ones. For example, the probability of receiving the correct diagnosis varied across the private and public provider, but never exceeded 15%, while the rate of correct treatment varied from 21% to about 50%. Doctors were more likely than other providers to provide the correct diagnosis. A great deal of treatment was inappropriate. CLAHRC West Midlands’ partner organisation in global health is conducting a study of service provision in slums with a view to devising affordable models of improving health care.[6]

— Richard Lilford, CLAHRC WM Director

References:

1. Watson SI, Wroe EB, Dunbar EL, et al. The impact of user fees on health services utilization and infectious disease diagnoses in Neno District, Malawi: a longitudinal, quasi-experimental study. BMC Health Serv Res. 2016; 16: 595.
2. Carrin G & Hanvoravongchai P. Provider payments and patient charges as policy tools for cost-containment: How successful are they in high-income countries? Hum Resour Health. 2003; 1: 6.
3. Brook RH, Ware JE, Rogers WH, et al. The effect of coinsurance on the health of adults. Results from the RAND Health Insurance Experiment. Santa Monica, CA: RAND Corporation, 1984.
4. Das J, Holla A, Mohpal A, Muralidharan K. Quality and Accountability in Healthcare Delivery: Audit-Study Evidence from Primary Care in India . Am Econ Rev. 2016; 106(12): 3765-99.
5. Lilford RJ. League Tables – Not Always Bad. NIHR CLAHRC West Midlands News Blog. 28 August 2015.
6. Lilford RJ. Between Policy and Practice – the Importance of Health Service Research in Low- and Middle-Income Countries. NIHR CLAHRC West Midlands News Blog. 27 January 2017.

Living on Less Than One Dollar per Day

Sam Watson recently drew my attention to this fascinating article by my heroes – Adhijit Banerjee and Esther Duflo.[1] How do people in the “bottom billion” spend an income of around $1 per day? The authors turn to household surveys covering 13 countries in Asia, Africa and Central America (one assembled by the World Bank, and the others by the RAND Corporation). Even though it is hard to get a full stomach on $1 per day and many are hungry, not all money is spent on food – the proportion varies from a half to three-quarters of income spent on food. Nor are the cheapest foods always selected – taste crowds out Calories, even if that leaves you hungry. The second largest source of expenditure is festivals, such as weddings and funerals. Radios are a priority and show elastic demand on income. There is an inverse relationship across countries between spending on radios and on festivals. Asset ownership is very low – even in rural areas bicycle ownership is low – at a third of households or less. Education attracts a very low proportion of expenditure; 2-3% of the household budget in Pakistan, for example. People often feel hungry, many are anaemic, and energy levels are low. Illness rates are high and anxiety common when compared to high income countries. I guess many are in a poverty trap and need a little help to get them out of it, but the results resonate with the Gospel of Matthew, ‘Man shall not live by bread alone’.

— Richard Lilford, CLAHRC WM Director

Reference:

1. Banerjee AV & Duflo E. The Economic Lives of the Poor. J Econ Perspect. 2007; 21(1): 141-67.

Do Cash Transfers to the Poor Encourage Feckless Behaviour?

In a brilliant working paper, Evans and Popova consider whether non-conditional cash transfers encourage people in low-income countries to increase their use of ‘temptation goods’, such as tobacco and alcohol.[1] Their systematic review found 19 studies. The answer to the question is ‘no’, there is no positive effect on consumption of temptation goods. This effect is confirmed if the analysis is confined to randomised trials. In fact the point estimate signifies lower consumption of the temptation goods in association cash transfers. The extra money provided by the cash transfers seems to be wisely invested, for example, in childhood education. Of course, this does not mean that there are no instances where someone (usually a man I am afraid) took money (which is usually given to a woman) in order to go drinking. But then, it is a poor heart that never rejoices!

— Richard Lilford, CLAHRC WM Director

Reference:

1. Evans DK, Popova A. Cash Transfers and Temptation Goods. University of Chicago, IL. 2016.

Neighbourhood Effects and Child Development – Long-Term Results of a RCT

Adults and children in high-income areas fare better than those in low-income areas, as we pointed out in a recent post. What would happen if families from low-income areas moved to high-income areas? This was evaluated in a famous experiment called the “Moving to Opportunity Experiment”, conducted in the 1990s in the USA. Families were randomised to receive or not receive a voucher that enabled them to move from a low to a higher income neighbourhood. Intention-to-treat (ITT) analysis showed that the opportunity to move was associated with improved physical and mental health among adults, despite the fact that only about half of the families in the intervention group availed themselves of the voucher. There were no effects on earnings or employment of these adults, but what about the development of the children? A further cut of the data relating to children has now been published,[1] again using ITT principles, and children under 13 years old when they were randomised to receive the voucher had much better prospects than controls. They were more likely to go to college and experienced substantially higher incomes than control children. However, children who were older when they had an opportunity to move experienced slightly negative effects, consistent with findings by CLAHRC WM.[2] In the older children it is speculated that the effects of disruption outweighed the benefits of the new neighbourhood on average. Inward migration did not appear to have any negative consequences for the receiving communities. This is a fascinating social experiment, up there with the original ‘Head Start’ study of an intervention to support poor, single mothers.[3] What are the policy corollaries? The finding supports policies to prevent the poor concentrating in ghettoes like the banlieues around Paris and the stark segregation of middle class people in gated compounds increasingly seen in African cities. Such policies are further supported by evidence from our last blog showing that the health or poor people was enhanced, rather than undermined, by proximity to richer families.

— Richard Lilford, CLAHRC WM Director

Reference:

1. Chetty R, Hendren N, Katz LF. The Effects of Exposure to Better Neighbourhoods on Children: New Evidence from the Moving to Opportunity Experiment. Am Econ Rev. 2016; 106(4): 855-902.
2. Singh SP, Winsper C, Wolke D, Bryson A. School Mobility and Prospective Pathways to Psychotic-like Symptoms in Early Adolescence: A Prospective Birth Cohort Study. J Am Acad Child Adol Psychiatry. 2014; 53(5): 518-27.
3. Currie J, & Thomas D. Does Head Start Make a Difference? Am Econ Rev. 1995; 85(3): 361-4.

Correlation between Schooling and Per Capita GDP Growth

Previous studies have found only a modest correlation between mean years of schooling and GDP growth in low- and medium-income countries (LMICs). But the educational content of a given number of school years varies enormously – on average, school leavers in Honduras are over an unconscionable six years behind their age-controlled peers in Singapore in Science and Maths competence. A recent paper from ‘Science’ [1] shows that it is school achievement that is important and in logistic regression accounts for over half of the variance between countries in growth rate, conditional on economic starting point, and the temporal relationships all but exclude reverse causality. Of course, it is possible that there is some other ingredient that causes both school and economic attainment in the high economic growth countries. The CLAHRC WM Director hypothesises that knowledge is not just knowledge – education has a deeper effect on the psyche leading to a more empathetic, altruistic person. As the old quote has it, “education is what is left after all the facts have been forgotten.” Is this hypothesis testable?

— Richard Lilford, CLAHRC WM Director

Reference:

1. Hanushek EA, & Woessmann L. Knowledge Capital, Growth, and the East Asian Miracle. Science. 2016; 51 (6271): 344-5.

How Many Doctors Do We Really Need?

In a previous post we blogged about the changing nature of medical practice: the influences of regulation, guidelines, sub-specialisation, and patient expectations. We mentioned skills substitution, whereby less experienced staff take on tasks previously carried out by doctors. We also mentioned the role of Information Technology, but shied away from discussing the implications for medical manpower. However, it seems important to ask whether Information Technology could reduce the need for medical input by increasing the scope for skill substitution. Some patients have complex needs or vague symptoms, and such patients we assume will need to be seen by someone with deep medical knowledge to underpin professional judgements, and to provide patients with such an informed account of the probable causes of their illness and the risks and benefits of viable options. But much of medicine is rather algorithmic. A patient presents with back pain – follow the guidelines and refer the patient if any ‘red flags’ appear, for example. Many of the criteria for referral and treatment are specified in guidelines. Meanwhile, computers increasingly find abnormal patterns in a patient’s data that the doctor has overlooked. Work in CLAHRC WM shows that many patients do not receive indicated medicines.[1] Health promotion can be delivered by nurse and routine follow-up cases triaged by Physician Assistants. A technician can be trained to perform many surgical operations, such as hernia repair and varicose vein removals, and Physician Assistants already administer anaesthetics safely in many parts of the world.[2] Surely we should re-define medicine to cover the cognitively demanding aspect of care and those where judgements must be made under considerable uncertainty.

In the USA they talk about “people working up to their license”. What they mean is that it is inefficient for people to work for extended periods at cognitive or skill levels well below those they have attained by virtue of their intellect and education. Working way below the level is not only inefficient, but deeply frustrating for the clinician involved, predisposing them to burn out. Use doctors to doctor, not to fill in forms and perform routine surgical operations.

We conclude by suggesting that there is a case for re-engineering medical care or at least articulating a forward vision. The next step is some careful modelling, informed by experts, to map patterns of practice, assign tasks to cognitive categories, and calculate manpower configurations that are both safe and economical. Such a process would likely identify a more specific, cognitively elite role for expensive personnel who have trained for 15 years to obtain their license. In turn, this may suggest that less people of this type will be needed in the future.

While high-income countries should address the question “how much should we reduce the medical workforce, if at all?”, low-income countries face the reciprocal question, “by how much should we increase the medical work-force?” Countries such as Kenya have only two doctors per 10,000 population, compared to 28 in the UK, and 25 in the United States.[3] Much of the shortfall is covered by other cadres, especially medical officers (who work independently), and nurses. Health personnel are strongly buttressed by community health workers, a type of health worker that we have discussed in previous posts.[4] [5] Information Technology is unsurprisingly very under-developed in low-income countries, although telemedicine is increasingly used. It is particularly difficult to attract doctors to work in rural areas, and there is the perennial issue of the medical brain drain. The time is thus propitious to consider carefully the human resource needs not just of high-, but also of low- and middle-income countries, and consider how these may be affected by improving Information Technology infrastructure.

— Richard Lilford, CLAHRC WM Director

Reference:

1. Wu J, Yao GL, Zhu S, Mohammed MA. Marshall T. Patient factors influencing the prescribing of lipid lowering drugs for primary prevention of cardiovascular disease in UK general practice: a national retrospective cohort study. PLoS One. 2013; 8(7): e67611.
2. Mullan F & Frehywot S. Non-Physician Clinicians in 47 Sub-Saharan African Countries. Lancet. 2007; 370: 2158-63.
3. World Health Organization. Health Workforce: Density of Physicians (total number per 1000 population): Latest available year. 2015.
4. Lilford RJ. Lay Community Health Workers. NIHR CLAHRC West Midlands News Blog. 10 April 2015.
5. Lilford RJ. An Intervention So Big You Can see it From Space. NIHR CLAHRC West Midlands News Blog. 4 December 2015.