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Diabetes Burden and Healthcare Barriers Among Cataract Patients in Imo State, Nigeria Cover

Diabetes Burden and Healthcare Barriers Among Cataract Patients in Imo State, Nigeria

Open Access
|Sep 2026

Figures & Tables

Table 1

Modified MIF socio‑economic status (SES) questionnaire.

This SES questionnaire is structured to use nine brief, context‑specific questions to further classify patients into low, middle, or high socio‑economic groups within the rural Nigerian communities of this study’s focus. The aim of the questionnaire is to assess multiple domains that reflect various aspects of daily living conditions among these individuals.

MODIFIED MIF SOCIO‑ECONOMIC STATUS (SES) QUESTIONNAIRE
QuestionResponse Options (Score)
  • 1. Highest level of education attained by yourself or any of your parents:

University (4)
Post‑secondary education/technical college/polytechnic (3)
Secondary education (2)
Primary education (1)
Pre‑primary education (0)
Never enrolled in school (–1)
  • 2. What type of home do you live in?

Own cement home (with stairs) (4)
Own cement home (bungalow) (3)
Own thatched/mud home (2)
Rent a home or apartment (1)
Shared family home (apartment/flat) (0)
Homeless (shelter/open streets) (–1)
  • 3. What personal electronics do you own? (1 point for each electronic owned)

Radio (1)
TV (1)
Cell phone (1)
Generator (1)
Cable or Satellite (1)
  • 4. Sleeping arrangements (number of people per bed):

1 per bed (4)
2 per bed (3)
3 per bed (2)
1–3 per mat/mattress on the floor/hard floor (1)
>3 people per bed (0)
  • 5. Main source of drinking water (score highest option given):

Vendor, bottled water, or personal bore hole (4)
Pipe, tap water, or shared bore hole (3)
Well water (2)
River, lake, or stream (1)
Rainwater (0)
  • 6. What type of toilet facilities does your household use?

Own toilet (manual/self‑flushing) (4)
Own toilet (flush with bucket) (3)
Shared toilet (<3 families) (2)
Own pit/latrine (1)
Public facilities (0)
None/bush (–1)
  • 7. Do you receive food from the following places?

Do you eat in a restaurant once or more a week? (4)
Do you shop at a supermarket once a week or more? (3)
None of the below (2)
Charity or food program (1)
Street aid and/or begging (0)
  • 8. How many times do you and/or your family eat chicken, meat or fish?

Every day (4)
More than 3 times a week (3)
Less than 3 times a week (2)
Once a week (1)
A few times a month (0)
  • 9. What is your main source of transportation?

By car (with air conditioning) (4)
By car (no air conditioning) (3)
On motorcycle (2)
On bicycle (1)
Public transport (0)
On foot (–1)
Socio‑economic status (SES) score: −4 to 11 Low; 12 to 23 Middle; 24 to 36 High
Figure 1

Direct associations with barriers to diabetes treatment scores.

This figure represents an infographic of significant direct associations between barriers to care and key socio‑economic factors among cataract patients. Patients with more barriers often faced financial difficulty and limited access to health needs, while those in private homes were associated with fewer barriers to care scores, and frequent medication consumption. Note: Significance denotes P values < 0.05.

Table 2

Diabetes burden questionnaire.

The diabetes burden questionnaire depicts a six‑question system to classify patients’ diabetes severity or risk as Low, Moderate, or High based on treatment adherence, prescribed therapy, and symptom burden.

DIABETES BURDEN QUESTIONNAIRE
QuestionResponse Options (Score)
  • 1. Which of these are you prescribed for your diabetes?

Injections (1)
Tablets (1)
Both injections and tablets (−1)
None (0)
  • 2. How often are you supposed to take your medications according to your doctor?

My doctor has not prescribed any diabetes medication (1)
Only on some days per week (1)
Once a week or monthly (0)
Daily, after every meal (–1)
  • 3. How often do you take your medications/injections?

Once a week or monthly (1)
Few times a week (0)
Daily or after every meal (–1)
  • 4. Do you have any symptoms due to your diabetes? Mark all that apply.

None (1)
Tingling/numbness of legs and fingers (0)
Blurry vision or dizziness (–1)
Ulcers or non‑healing wounds (–1)
  • 5. How often do your symptoms occur?

Few times a week (1)
Once a week or monthly (0)
Daily, after every meal (–1)
  • 6. Does your diabetes affect your choice of food?

No (0)
Yes (–1)
Diabetes Burden Score: −6 to –3 High; −2 to 1 Moderate; 2 to 5 Low
Table 3

Barriers‑to‑care questionnaire.

The barriers‑to‑care questionnaire aims to measure the impact of various practical, cultural, and health system barriers and subsequent interference with self‑management of diabetes among patients within rural Nigeria.

BARRIERS‑TO‑CARE QUESTIONNAIRE
QuestionResponse Options (Score)
  • 1. Do you have financial difficulty purchasing your diabetes medications?

No (1)
Sometimes (0)
Yes (−1)
  • 2. Are there people who live with you and take care of you at home?

Yes (1)
Sometimes (0)
No (−1)
  • 3. Do you need help to cook, clean, farm, or go to the market on your own?

No (1)
Sometimes (0)
Yes (−1)
  • 4. Do you smoke?

No (1)
I do not, but a family member I live with does (0)
Yes (−1)
  • 5. What barriers do you encounter when traveling to a clinic/pharmacy?

Do not have any issues walking or driving to the pharmacy (1)
Can walk, but live too far from the closest clinic/pharmacy (0)
Use a wheelchair or walking stick and no car (−1)
  • 6. How many times did you drink soda or take sweets in the past week?

<1 time per week (0)
Less than 3–4 times per week (1)
≥3–4 times per week (−1)
  • 7. Do you take any herbs/natural remedies to manage your diabetes?

Yes, specify (1)
No (0)
  • 8. How often do you forget to take your diabetes medications?

Never or once in a day (1)
2 or more times a week (0)
Stopped taking them (−1)
  • 9. How often do you check your blood glucose levels?

≥1 time per week (1)
Monthly (0)
<1 time per week (−1)
  • 10. How often do you exercise (e.g., bicycle riding, farming, jogging, dancing)?

Every day (1)
About 3–4 times per week (0)
<2 times per week (−1)
  • 11. What is the average range of your blood glucose levels?

Open response (no score)
  • 12. When was your last visit to a doctor?

<2 months (4)
3–6 months (3)
6–12 months (2)
1–3 years (1)
>3 years (0)
  • 13. Where do you obtain medication?

From a pharmacy/chemist with a prescription (3)
From a pharmacy/chemist without a prescription (2)
From a friend or relative (1)
Other, specify (0)
Barriers‑to‑care score: −9 to 0 High; 1 to 10 Moderate; 11 to 17 Low
Table 4

Post‑assessment clinical information.

The MIF original post‑assessment clinical information survey was used by clinicians to document systemic comorbidities and corresponding severity among patients within the study in a single standardized format to allow for comparison.

POST‑ASSESSMENT CLINICAL INFORMATION
SECTIONITEMDESCRIPTION/OPTIONS
MedicationsCurrent medications being takenOpen response
Laboratory/vitalsHemoglobin (Hgb)______ mg/dL
Blood pressure______ mmHg
Blood glucose______ mg/dL
Intraocular pressure (IOP) OD______ mmHg
Intraocular pressure (IOP) OS______ mmHg
Other findingsOtherOpen response
Cataracts stagingCataracts OD______ (mild: trace, 1+, 1–2+; mature: 3+ and higher; coding for dataset: mild = 0, mature = 1)
Cataracts OS______ (mild: trace, 1+, 1–2+; mature: 3+ and higher; coding for dataset: mild = 0, mature = 1)
Medical diagnosis (check all that apply)Diabetes
Anemia
Cataracts
Glaucoma
Hypertension
Other______________________________
Figure 2

Low hematocrit (HCT) levels and associated factors.

In this figure, an infographic summarizing key factors associated with lower HCT levels is shown. There was an association between low HCT and herb use (p = 0.041). Patients with low HCT were also more likely to have severe cataracts (p = 0.018), need help with daily tasks (p = 0.008), have lower hemoglobin levels (p < 0.001), and experience greater financial difficulty purchasing medications (p = 0.012). These findings suggest that anemia risk may be compounded by both clinical, behavioral, and socio‑economic challenges in this population.

Note: HCT denotes Hematocrit

Table 5

Drinking water source correlations.

This table examined correlations between drinking water source and any other significantly associated variables. Water source was scored ordinally, where higher scores indicate a private water source (with a score of 3 = pipe/tap/shared borehole; while 4 = vendor/bottled/personal borehole). The highest IOP’s 95% CI for Spearman’s r was computed using the DescTools package in R. Sample size (n) varied across the rows due to missing data for some individual variables. A p‑value of less than 0.05 was considered statistically significant. Highest IOP was defined as the greater value between IOP measured in the right eye (OD) and left eye (OS).

DRINKING WATER SOURCE CORRELATIONS
EXPOSUREASSOCIATED VARIABLENSPEARMAN R95% CIP‑VALUE
Private sources of drinking waterHigher SES score450.5320.274–0.718<0.0002
More personal electronics owned440.4600.180–0.6710.002
Improved toilet facilities450.4060.119–0.6300.006
Less frequent or no diabetes medications prescribed by their doctor230.5030.102–0.7640.014
Highest IOP220.5140.117–0.7690.015
Figure 3

Drinking water source and risk for glaucoma.

This figure depicts the association between drinking water source and the risk for glaucoma. Each bar shows the percentage of patients within a drinking‑water source category who are at risk for glaucoma, IOP greater than 21 mmHg in either eye. Patients using “Vendor, Bottled, or Personal Bore Hole” water had the highest glaucoma risk (83.33%), while Pipe/Tap or Shared Bore Hole users had the lowest observed glaucoma risk (27.27%). Bars are grouped by drinking‑water source, with glaucoma risk status indicated by color (blue for risk and orange for no risk). Note: IOP denotes intraocular pressure. Glaucoma risk is defined by IOP > 21 mmHg, while No Glaucoma risk is defined by IOP ≤ 21 mmHg.

Table 6

Partial correlation sensitivity analysis of the association between drinking water source and highest IOP after controlling for diabetes related confounders.

To determine whether the association between drinking water source and highest IOP could be explained by diabetes‑related factors or nutrition, partial Spearman correlations were run controlling for four covariates individually: prescribed medication use, medication adherence, measured blood glucose, and hemoglobin. The unadjusted correlation between water source and highest IOP was r = 0.51, p = 0.015, n = 22. After controlling for each covariate one at a time, the association remained statistically significant across all four models. Subsample sizes varied due to missing covariate data. The medication adherence model was based on a smaller subsample (n = 10), so a leave‑one‑out sensitivity analysis confirmed this result was not driven by any single case, with r ranging from 0.73 to 0.91 across all iterations. Highest IOP denotes greatest IOP taken from OS and OD; r = Spearman rank correlation coefficient.

CONFOUNDER CONTROLLEDUNADJUSTED RUNADJUSTED PADJUSTED RADJUSTED PCHANGE IN RNINTERPRETATION
Prescribed diabetes medication0.510.0150.480.040−0.0320Water source association to IOP remains significant after controlling for prescribed diabetes medications
Medication adherence0.510.0150.780.013+0.2710Water source association with IOP remain significant after controlling for medication adherence. This was based on a small subsample (n = 10); so a leave‑one‑out sensitivity analysis confirmed stability
(r ranged 0.73–0.91).
Measured blood glucose (mg/dL)0.510.0150.470.043−0.0420Water source association with IOP remains significant after controlling for measured blood glucose.
Hemoglobin levels (g/dL)0.510.0150.480.039−0.0320Water source association with IOP remains significant after controlling for hemoglobin as a proxy for nutrition.
Table 7

Statistical analyses assessing correlations between diabetes burden score and individual barriers to care after adjusting for social determinant risks as confounders.

BARRIER‑TO‑CARE VARIABLEβP‑VALUEINTERPRETATION
Do you have financial difficulty purchasing your diabetes medications?a0.6860.032*Having no financial difficulty purchasing medications was associated with a 0.686‑unit improvement in diabetes burden score, indicating lower diabetes burden.
Are there people who live with you and take care of you at home?b0.5920.055†Having in‑home caregivers was associated with a 0.592‑unit improvement in diabetes burden score, indicating lower diabetes burden, but to be interpreted with caution.
Do you need help to cook, clean, farm/go to the market on your own?0.3380.322No statistically significant association detected in this sample.
Do you smoke?0.3380.658No statistically significant association detected in this sample.
What barriers do you encounter when traveling to clinic/pharmacy?0.5000.258No statistically significant association detected in this sample.
How many times did you drink soda or take sweets in the last week?0.1610.798No statistically significant association detected in this sample.
Use of herbs or natural remedies0.3920.491No statistically significant association detected in this sample.
Frequency of forgetting diabetes medications−0.4700.374No statistically significant association detected in this sample.
Frequency of blood glucose self‑monitoring−0.4240.519Not statistically significant. Patients at higher risk may monitor more frequently, which could explain the change in direction.
How often do you exercise?0.5830.081†More frequent exercise was associated with a 0.583‑unit improvement in diabetes burden score, but did not reach statistical significance.
Time since last doctor visit0.1390.447No statistically significant association detected.
Where do you obtain medication?−0.5680.118No statistically significant association detected. Patients with more severe diabetes burden may use formal/prescribed sources more, which could explain the direction.

[i] a For financial difficulty, “No” = +1 coding; a positive β reflects that those without financial difficulty had a more positive diabetes burden score, which means lower burden.

[ii] b For in‑home caregivers, “Yes” = +1 coding, so a positive β reflects that those with caregivers had a more positive diabetes burden score, which means lower burden.

[iii] Linear regression models examined the association between each barrier‑to‑care variable and diabetes burden score after adjusting for type of home, main source of drinking water, and main source of transportation. This analysis tells us which specific barriers to care are associated with worse diabetes burden in this population after removing the influence of socio‑economic differences between patients, so that any association found reflects a consistent barrier itself rather than manifestations of the patient’s overall poverty level. Diabetes burden score grading and barriers‑to‑care response coding are reported in Tables 2 and 3, respectively. *Asterisk indicates statistically significant results (p < 0.05); †Daggers indicate results that approached but did not reach the α = 0.05 threshold. Non‑significant results may reflect limited statistical power due to sample size rather than a true absence of association. Note: β denotes the unstandardized regression coefficient.

Table 8

Conclusion table on key findings ranked by significance.

This table presents key findings from this study ranked by statistical strength and clinical significance, with the conclusion column addressing their relevance to diabetes burden and cataract outcomes among patients in Imo State, Nigeria. Where multiple associations contribute to a single finding, the strongest p‑value was used for ranking. The caregiver finding (Rank 3) did not reach conventional statistical significance for diabetes severity (p = 0.055) but is included given its borderline significance and two additional significant outcomes.

RANKKEY FINDINGSTATISTICAL EVIDENCECONCLUSION
1Private drinking water source is associated with elevated IOP, independent of medication behavior, glycemic status, hemoglobin levels, and overall SES.
  • Highest IOP: r = 0.51, p = 0.015, n = 22.

  • After controlling for medication use r = 0.48, p = 0.040, n = 20;

    After controlling for adherence r = 0.78, p = 0.013, n = 10;

    After controlling for blood glucose r = 0.47, p = 0.043, n = 20;

    After controlling for hemoglobin r = 0.48, p = 0.039, n = 20.

  • After controlling for overall SES score: r = 0.59, p = 0.005, n = 22.

Private drinking water was consistently associated with elevated IOP, making it the most novel finding in this study. The association held after controlling for medication behavior, glycemic status, hemoglobin, and overall SES, showing that water source has an independent association with IOP that cannot be explained by how well or poorly patients manage their diabetes or by general wealth. Water source and blood glucose were not significantly associated with each other (r = 0.13, p = 0.491, n = 32), and blood glucose was not significantly associated with IOP (r = −0.44, p = 0.089, n = 16), suggesting that glycemic status alone doesn’t explain the link between water source and IOP. Therefore, water source may represent a genuinely independent environmental concern for glaucoma risk in this population that warrants further investigation.
2Financial difficulty purchasing medications associated with worse diabetes burden.
  • β = 0.686, p = 0.032 (adjusted regression).

Financial difficulty was the strongest and only statistically significant predictor of diabetes burden in SES‑adjusted analyses. When patients cannot afford medications, their blood glucose stays elevated, which speeds up damage to the blood vessels supplying the lens and retina. This barrier also compounded others; patients with financial difficulty were less likely to have caregivers at home and more likely to need help with daily tasks.
3Caregiver presence associated with better diabetes burden, lower barriers to care, and higher hemoglobin simultaneously.
  • Diabetes burden: p = 0.055, β = 0.592.

  • Barriers to care: r = 0.327, p = 0.030, n = 36.

  • Hemoglobin: r = 0.463, p = 0.003, n = 36.

Caregiver presence was the only factor in this study linked to better outcomes across all three domains at once. Patients with caregivers had better diabetes management, fewer barriers to care, and healthier Hgb. Each of these pathways connects back to cataract risk since better diabetes management slows cataract lens clouding, fewer barriers means more consistent care, and improved hemoglobin reduces the compounding effect of anemia on blood vessels already damaged by diabetes.
4Low hematocrit directly associated with more advanced cataracts.
  • Hematocrit and cataract severity: r = −0.742, p = 0.018, n = 12.

  • Hemoglobin and hematocrit: r = 0.952, p < 0.001.

Low hematocrit was directly linked to more advanced cataracts in this sample. When a diabetic patient also has anemia, the blood carries less oxygen to the already compromised vessels supplying the lens and retina. This double burden of diabetic vascular damage and reduced oxygen delivery accelerates lens clouding. Addressing anemia through supplements and locally available nutritional interventions is one of the most modifiable clinical actions in this study.
5Need for daily task help associated with advanced cataracts; transportation limitations linked to lower SES and greater struggles farming, cooking, cleaning, and traveling for food.
  • Daily task help and cataracts: r = −0.452, p = 0.030.

  • Transport limitation and lower SES: r = 0.371, p = 0.012.

  • Transport limitation and daily task help: r = 0.424, p = 0.004.

Patients who needed help with daily tasks had more advanced cataracts despite having seen a doctor recently. The issue may not be solely primary care access, but access to an eye specialist. Patients with limited transportation were also more likely to struggle with daily tasks, and those daily task limitations were linked to worse cataract outcomes. Bringing specialist eye care closer to the community is the most direct way to address this.
6Herb use associated with lower hematocrit (HCT).
  • r = −0.473, p = 0.041.

Herb users were more likely to have lower HCT, which is directly linked to worse cataract outcomes in this sample. Herb use likely reflects socio‑economic living conditions.
Since the use of herbs with metformin and sulfonylureas are not currently monitored in this setting, there is a risk of undetected effects on blood glucose and HCT that should be addressed during doctor visits.
7Patients in privately owned homes reported more daily medication use. Patients on more frequent medications had worse diabetes burden, but diabetes burden itself did not differ by socio‑economic status, suggesting that medication use reflects disease severity rather than socio‑economic advantage.
  • Privately owned homes and more daily medication adherence: (r = −0.528, p = 0.017).

  • Prescribed daily medications and worse diabetes burden scores: r = 0.661, p < 0.001, n = 23.

  • Self‑reported daily medication adherence and worse diabetes burden scores r = 0.68, p < 0.001, n = 20.

  • Number of chronic conditions and diabetes burden: r = −0.41, p = 0.008, n = 40.

  • Diabetes burden by home ownership group: Non‑cement home median = 0 (IQR = 2) vs Owned cement home median = 0 (IQR = 3), p = 0.563. Mann‑Whitney U test: p = 0.563 (not significant).

  • Diabetes burden by water source group: Pipe/tap/shared borehole median = −1 (IQR = 2) vs Vendor/bottled/personal borehole median = 0 (IQR = 3), p = 0.899. Mann‑Whitney U test: p = 0.899 (not significant).

  • Diabetes burden vs overall SES score: r = −0.07, p = 0.667, n = 45.

Patients in privately owned homes took diabetes medications more consistently than those in rented or shared homes. However, diabetes burden did not differ significantly between the two home ownership groups, nor by water source or overall SES score, meaning higher SES patients were not significantly less burdened by their diabetes.
Across the full sample, patients who took medication most frequently and consistently had the worst diabetes burden, not the best, and this pattern held regardless of socio‑economic status. So, while better housing was linked to more consistent medication use, it was not linked to less severe diabetes, suggesting that adherence in this sample may simply reflect the need to manage more severe disease rather than leading to better control.
Table 9

Modifiable aspects of this healthcare system to improve comorbidity burden and cataract outcomes.

The modifiable domains included in this table aim to improve cataract outcomes with respect to diabetes burden, anemia risk, and glaucoma risk among patients in Imo State, southeastern Nigeria. The recommended interventions are based on study findings and tailored to the local healthcare infrastructure and community practices for feasibility. Domains 1 through 5 address the barriers most prevalent among lower SES patients, where access and resource limitations contribute most to diabetes burden. Domains 6 through 9 address the factors most relevant to higher SES patients, where diabetes management behaviors and glaucoma risk are the main concerns.

MODIFIABLE ASPECTS OF THIS HEALTHCARE SYSTEM TO IMPROVE COMORBIDITY BURDEN AND CATARACT OUTCOMES
MODIFIABLE DOMAINSTUDY FINDINGRECOMMENDED INTERVENTIONANTICIPATED BENEFITINTERVENTION LEVEL
Lower SES Patients: Diabetes Burden and Resource Barriers impacting Cataract Outcomes
1. Medication access
Financial difficulty (Purchasing diabetes medications)Significant predictor of diabetes severity (β = 0.686, p = 0.032). Also linked to lower caregiver support and more need for daily task help.
  • Collaborate and support enrolment into the recent 2025 Imo State Health Insurance Agency (IMSHIA) program, which covers metformin and sulfonylureas at a subsidized cost.

  • Follow up with patients between missions to inform them of MIF medication subsidies also available during the year to support continuity of their care.

  • Better medication access improves adherence, which lowers blood glucose over time.

  • Sustained glycemic control slows lens opacity progression and reduces cataract risk.

  • Addresses the most significant modifiable predictor of diabetes burden found in this study.

Clinical system /Policy/Hospitals and pharmacies
2. Nutrition and anemia
Limited animal protein intake; low hemoglobin and hematocrit levelsLow Hgb and HCT were co‑associated. HCT associated with more advanced cataracts and greater difficulty with daily tasks. When a diabetic patient also has anemia, the damage diabetes causes to blood vessels gets worse, and the eyes and lens are directly affected.
  • Crayfish: already used daily in Igbo cooking, and provides heme iron, zinc, and complete protein at very low cost.

  • Moringa (okwe nri): grows locally in southeast Nigeria, is high in iron, vitamin C, and folate, and is added to local soups e.g., egusi soup.

  • Cowpeas and African breadfruit (ukwa): low glycemic index, high fiber, and affordable. Soaking cowpeas overnight before cooking improves iron absorption.

  • Subsidized protein programs for fish and chicken, local food partnerships, or monthly diabetes‑friendly farmer’s market events with incentives for local poultry and fish farmers.

  • Vitamin B12 supplements because plant foods alone cannot provide adequate B12. Low‑cost supplements are already co‑prescribed at other Nigerian diabetes clinics, so this is feasible to add to routine care visits.

  • Vitamin A: moringa and red palm oil used in moderation are locally accessible and may support eye health.

  • Treating iron deficiency reduces the burden of diabetic vascular damage and reduced oxygen delivery to the lens and retina.

  • B12 supplements reduce nerve damage that compounds diabetic neuropathy.

  • Low glycemic index food substitutes improve blood sugar management without requiring medication changes.

  • Vitamin A is independently linked to lower cataract risk.

Community/Policy
3. Caregiver and social support
Absence of in‑home caregiver supportCaregiver presence linked to better diabetes severity score (p = 0.055), lower barriers to care score (r = 0.415, p = 0.005), and higher hemoglobin (r = 0.46, p = 0.003) simultaneously.
  • Work with local churches and community groups to set up informal caregiver volunteer programs for patients who live alone.

  • Consider paid family caregiver models similar to programs in the United States, where family members are compensated for caring for elderly relatives. This may also create economic opportunity for younger generations with limited formal employment.

  • Train caregivers in medication reminders, help attending clinic appts., and basic dietary guidance during a single mission session.

  • Develop a mobile platform or compile phone resources so caregivers can access guidance on minor issues remotely to reduce unnecessary travel.

  • Assign community health workers to patients with no caregiver, low SES, and limited transportation.

  • Caregiver support is the only finding in this study associated with better diabetes outcomes, lower barriers, and better hemoglobin levels at the same time.

  • Better medication management and nutrition together lower diabetes severity, which directly improves cataract surgery access and outcomes.

  • Paid caregiver models may create economic opportunity while sustaining patient support.

  • Remote access tools reduce transportation burden for minor concerns between missions.

Community/System/Policy
4. Transportation and healthcare access
Limited transportation optionsPatients with transportation limitations had advanced cataracts despite recent doctor visits, suggesting the issue may be access to an eye care specialist rather than primary care.
  • Use a mobile clinic model to bring diabetes screening and medication dispensing directly to the community.

  • Schedule clinic days to match local market days when transport is naturally more available or conduct weekly screenings after church‑service

  • Offer transport vouchers or coordinate with local churches to provide rides on clinic days.

  • Introduce medication delivery as an alternative to pharmacy pickup for patients with severe mobility limitations.

  • Prioritize home visits by community health workers for patients with advanced cataracts who cannot travel independently.

  • Bringing care to the community increases clinic attendance and medication refill rates.

  • Earlier cataract care leads to better surgical outcomes and preserved vision.

System Community
5. Culturally sensitive clinical care
Regulated herb use; anemia screeningPatients managing their diabetes with “onugbu” were more likely to have lower hematocrit. Herb and drug interactions are not currently monitored in this setting.
  • Ask about specific and detailed herb use at every diabetes visit.

  • Counsel patients on common herb interactions with metformin and sulfonylureas. Train local pharmacists to provide accessible guidance on safe herbal remedy use alongside prescribed medications.

  • Add hemoglobin or hematocrit testing to routine diabetes care visits using a point‑of‑care device.

  • Track missed doctor visits among patients with dietary limitations using simple community health worker checklists and follow up with home visits for high‑risk patients.

  • Finding low hematocrit early means anemia can be caught and treated before it starts affecting vision or nerve function.

  • Teaching patients about herb interactions helps prevent hidden effects on blood sugar and blood cell levels that no one would otherwise know about.

Clinical system
6. Higher SES patients: glaucoma risk and management of comorbidities impacting cataract outcomes
Private drinking water source associated with elevated IOPPrivate water access associated with higher SES yet higher IOP in both eyes (r = 0.51, p = 0.015). Novel finding suggesting environmental exposure may contribute to glaucoma risk beyond traditional clinical factors.
  • Introduce routine IOP screening as part of diabetes care visits for higher SES patients. Glaucoma risk is disproportionately high in West African populations and is often asymptomatic until advanced.

  • Test private borehole water sources for specific mineral content and potential contaminants using low‑cost community water testing kits.

  • Recommend low‑cost household filtration options for patients using private borehole sources.

  • Develop community education on water safety and its potential relationship to eye health, in partnership with local environmental monitoring programs.

  • Routine IOP monitoring enables earlier detection of glaucoma risk before vision loss occurs.

  • Identifying environmental contributors to elevated IOP opens a novel prevention pathway specific to this population.

  • Water safety education addresses a modifiable exposure that may affect outcomes beyond eye health.

Community/Policy
7. Exercise and lifestyle
Low exercise frequency among medicated patientsHigher SES patients prescribed daily medications were less likely to exercise weekly (r = 0.47, p = 0.029), suggesting medication alone is not translating into healthier behaviors.
  • Incorporate structured physical activity counseling into diabetes care visits. Recommend culturally familiar activities such as farming, walking, or dancing that are already part of daily life.

  • Provide simple written or illustrated exercise tips that patients can take home and share with their caregivers.

  • Use community health workers to follow up on lifestyle goals like exercise frequency during visits.

  • Regular exercise improves insulin sensitivity and lowers blood glucose independently of medication.

  • Physical activity reduces cardiovascular risk factors that compound diabetic eye disease.

  • Addressing sedentary behavior in medicated patients may gain diabetes improvements that medication alone is not achieving.

Clinical/Community
8. Diabetes management quality
High diabetes severity despite daily medication usePatients on medications still had high diabetes burden scores, more chronic diseases, and frequent symptoms including blurry vision, dizziness, ulcers, and non‑healing wounds (r = 0.64, p < 0.00003).
  • Review medication regimens for patients with high diabetes severity scores despite reported adherence. Consider whether their current first‑line agents are adequate or need escalation.

  • Introduce structured symptom tracking at every visit. Blurry vision, dizziness, ulcers, and non‑healing wounds should be documented as indicators of disease progression.

  • Coordinate referrals to specialist care for patients with multiple chronic diseases and persistent symptoms.

  • Provide patients with simple “symptom diaries” or mobile‑based tools to track daily symptoms between visits.

  • Identifying patients whose disease is progressing despite medication allows us to review their regimen on time before complications

  • Tracking blurry vision systematically creates a direct link to timely eye care referral.

Clinical system
9. Blood glucose self‑monitoring
Infrequent blood glucose self‑monitoring81% of patients (n = 29/36) reported monitoring less than once per week. No significant association was found between monitoring frequency and IOP, r = −0.25, p = 0.297, or any other clinical variable in this dataset.
  • Provide affordable glucometers and test strips to patients who lack home monitoring equipment.

  • Educate patients on the importance of regular blood glucose self‑monitoring as part of standard diabetes self‑management.

  • More frequent monitoring allows patients to detect and respond to glycemic fluctuations.

Clinical/Community
DOI: https://doi.org/10.5334/aogh.5168 | Journal eISSN: 2214-9996
Language: English
Page range: 90 - 90
Submitted on: Jan 25, 2026
Accepted on: Aug 8, 2026
Published on: Sep 7, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services

© 2026 Kamsi Oparaugo, Parker Cox, Andrey Kharlamov, Kelechi Mezu-Nnabue, Udo Ubani, Olachi J. Mezu-Ndubuisi, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.