Dear Editor,
We have read with great interest the scoping review by Lee et al.[1], investigating efforts to integrate eye care into healthcare systems in low-income and middle-income countries (LMIC). We also share a belief in the importance of further integrating service delivery in this area.
Most of the cited studies addressed the integration of eye care into wider healthcare settings. The review highlights improved outcomes in management of retinopathy of prematurity achieved through enhancements to paediatrician-led care [2]. Furthermore, the review displays how improved provision of resources, training and enhanced engagement with other stakeholders has resulted in improved knowledge and guideline usage in primary care [3].
The review highlighted various interventions delivered as short training sessions or educational modules [4-6]. Whilst undeniably important for capacity building, these measures cannot in and of themselves achieve a highly integrated system, which would require changes in infrastructure, guidelines and referral systems.
We would appreciate a further understanding of the authors’ rationale for including certain studies in the review, which appear to contain remote or indirect references to eye care service delivery [7-12]. Some included studies [11, 12] appear to link improved management of hypertension and diabetes with prevention of associated retinopathy, though the articles do not appear to mention diabetic retinopathy explicitly.
A previous scoping review [13] concerns integrating eye health into primary healthcare in Africa, and makes wide-ranging points regarding changes to guidelines, protocols, governance, equipment and leadership – this literature may have been useful to reference in the current article.

The review by Lee et al. [1] highlights a few examples of successful integration of vision care into primary healthcare. This is understandable, given the significant pressures faced by primary healthcare globally. An alternative may be to explore integrating primary healthcare services into existing vision care platforms.
India, for example, has arguably the highest cataract surgical rate (CSR) of any lower- middle-income country, rivalling many high-income countries at 6000/million population/year. This salient strength of the healthcare system could potentially be leveraged in order to build capacity for the delivery of primary health care. Excellent eye care networks are available in India through organisations such as L V Prasad Eye Institute (LVPEI) and Aravind Eye Care[14][15]. LVPEI consists of 282 centres that provide extensive coverage, including remote and rural communities. LVPEI has a self-sustaining financial model, and the majority of patients have their care provided free of cost [15]. This excess capacity could potentially be harnessed not only to help support the delivery of primary care, but potentially to incentivise acceptance of care for chronic conditions through delivery of free eye care.
One key service for vision centres to deliver may be screening of non-communicable diseases (NCDs). Low-cost and rapid screening of blood pressure, fingerstick blood glucose, hearing and dental health could all be undertaken in existing vision centres. Furthermore, there lies the potential for eye care providers to ask for demonstrable proof of adherence to NCD care regimens as advised by medical professionals, and for this to be treated as a prerequisite for provision of vision care that is free of charge.
Several challenges must be overcome for this integration to be successfully implemented. There is a strong need to prove that adoption of these additional services by vision centres will not overburden the system and pose a threat to the sustainability of these networks. There is a need to identify the key priorities of care needed in each community and focus integration efforts on tackling the aspects of care that are most required. Additionally, high-quality trials would be needed, assessing community involvement and uptake of services under a new model of integration.

1. Lee, L., et al., Integrating eye care in low-income and middle-income settings: a scoping review. BMJ Open, 2023. 13(5): p. e068348.
2. Jacoby, M.R. and L. Du Toit, Screening for retinopathy of prematurity in a provincial hospital in Port Elizabeth, South Africa. S Afr Med J, 2016. 106(6).
3. Lilian, R.R., et al., Strengthening primary eye care in South Africa: An assessment of services and prospective evaluation of a health systems support package. PLoS One, 2018. 13(5): p. e0197432.
4. Mafwiri, M.M., et al., Mixed methods evaluation of a primary eye care training programme for primary health workers in Morogoro Tanzania. BMC Nurs, 2016. 15: p. 41.
5. Mafwiri, M.M., R. Kisenge, and C.E. Gilbert, A pilot study to evaluate incorporating eye care for children into reproductive and child health services in Dar-es-Salaam, Tanzania: a historical comparison study. BMC Nurs, 2014. 13: p. 15.
6. Malik, A.N.J., et al., Integrating eye health training into the primary child healthcare programme in Tanzania: a pre-training and post-training study. BMJ Paediatr Open, 2020. 4(1): p. e000629.
7. Ajay, V.S., et al., Development of a Smartphone-Enabled Hypertension and Diabetes Mellitus Management Package to Facilitate Evidence-Based Care Delivery in Primary Healthcare Facilities in India: The mPower Heart Project. J Am Heart Assoc, 2016. 5(12).
8. Abebe, A.M., M.W. Kassaw, and F.A. Mengistu, Assessment of Factors Affecting the Implementation of Integrated Management of Neonatal and Childhood Illness for Treatment of under Five Children by Health Professional in Health Care Facilities in Yifat Cluster in North Shewa Zone, Amhara Region, Ethiopia. Int J Pediatr, 2019. 2019: p. 9474612.
9. Carnell, M.A., et al., Effectiveness of scaling up the 'three pillars' approach to accelerating MDG 4 progress in Ethiopia. J Health Popul Nutr, 2014. 32(4): p. 549-63.
10. Jimenez Carrillo, M., et al., Comprehensive primary health care and non-communicable diseases management: a case study of El Salvador. International Journal for Equity in Health, 2020. 19(1): p. 50.
11. Lall, D., et al., Improving primary care for diabetes and hypertension: findings from implementation research in rural South India. BMJ Open, 2020. 10(12): p. e040271.
12. Lebina, L., et al., Process evaluation of implementation fidelity of the integrated chronic disease management model in two districts, South Africa. BMC Health Serv Res, 2019. 19(1): p. 965.
13. du Toit, R., et al., Evidence for integrating eye health into primary health care in Africa: a health systems strengthening approach. BMC Health Services Research, 2013. 13(1): p. 102.
14. Mehta MC, Narayanan R, Thomas Aretz H, Khanna R, Rao GN. The L V Prasad Eye Institute: A comprehensive case study of excellent and equitable eye care. Healthc (Amst). 2020 Mar;8(1):100408. doi: 10.1016/j.hjdsi.2019.100408. Epub 2020 Jan 14. PMID: 31948870.
15. Namperumalsamy P. Maintaining quality in community eye care - The Aravind model. Indian J Ophthalmol. 2020 Feb;68(2):285-287.

In this review paper on the accuracy of gut feeling in diagnosing cancer in primary care, the authors conclude that “the findings support the continued and expanded use of gut feeling items in UK cancer referral pathways”. Whilst most clinicians will have experienced the gut feeling that their patient has a serious illness and be thankful for having done so, there is debate over what a gut feeling is and what it represents within the diagnostic process. Is it a subjective intuitive experience, or an objective recognition and response to an abnormal finding? In our teaching programme on ways to prevent diagnostic errors, we discuss the role that gut feeling has in the prevention of these errors through subconsciously alerting the clinician to the presence of an abnormal finding that has not been recognised by the cognitive part of the diagnostic process, or responded to by the clinician. To help explain this, gut feeling is conceptualised as representing a “twitch” of the clinician’s “medical antennae”.

The basis for this concept is as follows: All clinicians possess a set of medical antennae that twitch in response to an abnormal finding. Medical antennae function within the dual-process diagnostic reasoning model, where they monitor information as it is being gathered and processed. Their role within the diagnostic process is to act as a back-up warning system to alert the clinician to an abnormal finding that has not been recognised or responded to. It is postulated that antennae twitch is a subconscious response to a finding that is not present within the normal pattern of illness for a minor illness, or one that is present within the typical, atypical, or early pattern of a serious illness. Antennae twitch is experienced by the clinician as a feeling of unease, prompting a review the patient’s findings to establish the cause of this feeling. Therefore, antennae twitch is a subjective response to an objective finding. This establishes the important principle that the performance of the medical antennae is determined by the clinician’s knowledge of those findings, the sensitivity of the clinician’s antennae and the responsivity of the clinician.

Antennae performance, sensitivity and responsivity are important because they introduce the concept that there are factors that may positively and negatively affect whether a clinician experiences a gut feeling. For example, since antennae twitch is a response to a specific finding, learning what those specific findings are increases the likelihood that a twitch will occur. Conversely, if the clinician has not learned about or encountered one of those findings previously, they cannot experience a twitch. Antennae sensitivity and clinician responsivity can be compromised by both internal and external factors, such as tiredness, illness, mood disorders, workload, distractions and burn-out, resulting in “antennae droop”. Learning how to recognise and manage these periods of increased diagnostic risk safely has the potential to reduce the risk of diagnostic errors during consultations.

There are 4 ways in which the concept of medical antennae can contribute to the debate about gut feeling. Firstly, it postulates a mechanism by which gut feeling works in alerting the clinician to an abnormal finding. Secondly, it highlights the important role that gut feeling plays in identifying patients with serious illness and in preventing diagnostic errors. Thirdly, it treats gut feeling as a diagnostic tool, inferring that its performance can be enhanced through focused learning and the use of strategies to manage antennae droop safely. Finally, it is hoped that it will serve to stimulate further debate and research into whether gut feeling can be regarded as a diagnostic tool and taught as a diagnostic skill.

We thank the authors for their interest in our study. The authors highlight the considerable proportion of cases being identified by a single informant as a problem for data quality. However, this is not unusual considering that suicides and attempted suicides are associated with high stigma and hence there is very little discussion in the community. Most of these single informants were public health workers (Health or ICDS or Asha workers) who regularly visit and interact with families. Hence, they are more likely to have knowledge of events such as attempted suicide and suicides which are not otherwise in the public domain for other community informants. We have also conducted additional validity exercises where cases reported by single informants were cross-checked with the records maintained by Talati (village revenue officers) to ascertain the accuracy of the reported data.
We agree with the authors that further studies in other geographic locations will be ideal to identify the challenges in scaling up the surveillance system to the entire country.
We also agree with the authors that some legislative or policy measures to mandate data sharing by private health institutions are needed for effective implementation of community surveillance.
We agree with the authors when data on police records for suicidal attempts is n= 0 cases, the post hoc McNemar’s test is not the most appropriate statistical measure. Where we say “Whereas there was statistical evidence of a difference (p<0.01) between community surveillance and police records, and hospital records and police records” we should actually have said “Whereas the further statistical difference between community surveillance and police records, and hospital records and police records could not be tested as there were zero cases recorded by police”.
We did not explore alternative approaches such as imputing missing values as we are not certain that using imputation for real time data on suicides is an appropriate method for dealing with missing values.

Yang et al. conducted a meta-analysis of randomised controlled trials (RCTs) to investigate the effect of continuous positive airway pressure (CPAP) treatment on cognitive function in stroke patients with obstructive sleep apnoea (OSA) (1). There were no significant effects on global cognitive gain in stroke patients with OSA, although an early start within 2 weeks post stroke of CPAP treatment after stroke significantly improved global cognition. Additionally, CPAP did not significantly improve memory, language, attention or executive function. Stroke event itself may be a risk of poor cognitive function, and there is a need of understanding the clinical benefits of CPAP treatment on cognitive function in subjects without stroke history. I present information regarding the relationship between CPAP treatment and cognitive impairment with special reference to baseline state of cognitive function and CPAP adherence.

First, there was insufficient evidence to suggest that treating sleep dysfunction can improve cognition by a meta-analysis of RCTs (2). Yang et al. should consider the severity of stroke for the analysis, although stroke itself might contribute to the level of cognitive function.

Second, Hoyos et al. evaluated the cognitive benefits by CPAP treatment in patients with OSA and mild cognitive impairment (MCI) (3). The authors conducted RCT study to specify the causal association, and CPAP treatment was significantly associated with improvements in Pittsburgh sleep quality index, verbal learning and memory retention, which was inconsistent with data by Yang et al. Although the number of samples is limited, CPAP treatment suppressed the progression of cognitive impairment.

Finally, Liguori et al. evaluated the long-term effects of CPAP treatment in 24 patients with OSA and MCI (n=8) or Alzheimer's disease (AD: n=16) (4). There was a significant difference in cognitive and functional performances assessed with clinical dementia rating (CDR) scale between the CPAP non-adherent and adherent groups, presenting worse cognitive and functional performances in patients with CPAP non-adherent group. Although there were clinical advantages of CPAP treatment to delay cognitive deterioration in patients with MCI or AD, adherence to CPAP treatment may be a key factor to achieve risk reduction in the progression of cognitive impairment. Dunietz et al. also reported that PAP adherence was significantly associated with lower odds of incident diagnoses of AD (5). These two papers adopted a retrospective cohort study design, and RCTs are needed to specify the causal relationship.

References
1. Yang Y, Wu W, Huang H, et al. Effect of CPAP on cognitive function in stroke patients with obstructive sleep apnoea: a meta-analysis of randomised controlled trials. BMJ Open 2023;13(1):e060166.
2. Franks KH, Rowsthorn E, Nicolazzo J, et al. The treatment of sleep dysfunction to improve cognitive function: A meta-analysis of randomized controlled trials. Sleep Med 2023;101:118-26.
3. Hoyos CM, Cross NE, Terpening Z, et al. Continuous positive airway pressure for cognition in sleep apnea and mild cognitive impairment: A pilot randomized crossover clinical trial. Am J Respir Crit Care Med 2022;205(12):1479-82.
4. Liguori C, Cremascoli R, Maestri M, et al. Obstructive sleep apnea syndrome and Alzheimer's disease pathology: may continuous positive airway pressure treatment delay cognitive deterioration? Sleep Breath 2021;25(4):2135-9.
5. Dunietz GL, Chervin RD, Burke JF, et al. Obstructive sleep apnea treatment and dementia risk in older adults. Sleep 2021;44(9):zsab076.