Morbidity profile and health-seeking behavior of rural elderly near Vellore, India

ABSTRACT

The elderly population in India is growing, which presents a set of unique challenges for healthcare providers. Social changes in India are creating unique burdens for the Indian healthcare system that will shift elder care from family to other sources of care. In order to best serve the elderly of India, healthcare providers can be helped by information about patients with unmet needs. Health-seeking behavior (HSB) among Indian elderly can be a useful tool for healthcare providers seeking to allocate resources and serve their communities effectively. To better understand HSB for elderly in rural South India, we performed a cross-sectional study of HSB among elderly of age 60 and above in three rural villages near Vellore, India. We found a 93% prevalence of HSB for acute and chronic illnesses in our study population. Our data suggest that patients with certain chronic diseases may be less likely to seek treatment. We also found that the odds of being disease-free were 2.85 times more likely for males, compared to females. Finally, we saw no relationship between patients’ type of disease and their choice of government-run or private care facilities. Taken together, these data demonstrate a high level of community engagement by public health workers in the Vellore area. Our data can help inform local providers about potential gender differences in community health and disease types associated with low HSB. Context: Rural areas near Vellore, Tamil Nadu, India.

Aims:

To support public health efforts of local healthcare teams.

Settings and Design:

A cross-sectional study of HSB among rural elderly age 60+, located in three rural villages near Vellore, Tamil Nadu.

Methods and Material:

Participants included residents 60 years and older living in rural villages near Vellore. We asked through in-person interviews about demographics, chronic and acute morbidities, and HSB. Their answers were reviewed and analyzed with the statistical software package “R.”

Results:

Our participants near Vellore exhibited a high proportion of HSB, with 93% of affected individuals choosing to seek care. Despite the large number of different socioeconomic variables we studied, the only significant predictor of disease presence was gender, with males exhibiting much lower odds of reporting disease compared to females. We found no relationship between the type of illness and the type of healthcare facility approached for treatment. Finally, we found evidence of a relationship between the type of chronic illness suffered by a participant and whether the participant sought treatment at all.

Conclusions:

Public health efforts in the Vellore area have been highly successful compared to other parts of India and the world. Local providers may continue to improve patient engagement with healthcare by studying gender trends in morbidities and focusing on patients with less-common diseases.

Introduction

Elderly people in India, defined as persons aged 60 and over, are a growing subpopulation due to advances in life expectancy, reduced fertility, and other factors.[1,2,3] Policymakers and healthcare systems will be required to adapt as this vulnerable demographic becomes a larger share of the patient population.[4,5] This growth is not unique to India; the global population is projected to age in the upcoming decades.[6] However, India is undergoing economic and cultural changes that will bring unique burdens for its healthcare system as the nuclear family becomes a less common source of elder care in India.[5]

Helpful studies of health-seeking behavior (HSB) among the elderly have been done in various locales of the Indian subcontinent, including North India.[3,7,8,9] Furthermore, important work has been done with more general surveys that provide an overview of elderly HSB in India as a whole.[1,5,10] However, there is limited data on HSB for the elderly in rural South India. Rural elderly are at especially high risk for inadequate HSB when compared to urban elderly, and 67% of India’s elderly live in rural areas.[3,10,11] A rural/urban difference in HSB has been found in studies in Africa and China.[12,13,14] These findings suggest that the phenomenon of at-risk rural elderly is a global public health problem.

The causes of this problem may vary greatly between locations. For example, some rural Chinese elderly avoid healthcare largely because of a perception that they do not deserve care.[13,14] By contrast, many Shimla Hills elderly do not obtain care because they are physically unable to access care providers.[3] Because of these differences, healthcare providers everywhere require precise information that is specific to their environment.

To fill the information gap with regards to HSB among the elderly in rural South India, we performed a descriptive cross-sectional study among the elderly aged 60 and above in three rural villages near Vellore. We hypothesized that elderly people will seek healthcare when health resources are available and accessible, when the elderly are aware of health resources near them, or if health issues limit normal daily functioning. To this end, we sought to assemble a body of data that describes HSB among rural elderly near Vellore, India.

Subjects and Methods

Ethics

Our study was carried out under the approval of the Institutional Review Board of Christian Medical College (CMC), Vellore, and in accordance with the Helsinki Declaration of 1975, as revised in 2000.

Time and place

Three villages (Thutipet, Chinnampalampakkam, and Mottupalayam) were selected from Kaniyambadi block, Tamil Nadu, for this study. Villages were selected according to local expert opinion as reasonable representations of the rural elderly population in the Kaniyambadi block. The primary data were collected over 3 weeks of survey work within the months of June through August 2014.

Inclusion/Exclusion Criteria, Informed Consent:

Our eligibility criteria included residents 60 years and older of the three selected villages in the Kaniyambadi block who were able to communicate or had caregivers who could provide reliable information. We excluded those who refused to participate and the elderly with communication problems who did not have a caregiver able to provide reliable information. Signed informed consent was acquired from each interviewee prior to the administration of each questionnaire and interview. Interviewees who could not write signed with a thumbprint instead.

Participant selection

The sample population size was calculated based on the equation n = 4 pq/d2, to give a sample size of 100 (P = 50%, d = 20%). Due to the staged sampling method, a design effect of 2 was applied, giving a total sample size of 200 subjects. The roster from the CMC-Community Health Department database for three villages was divided into groups of at least 67 patients per group. In each village, one group was chosen to be interviewed. If an individual from a group could not be found after a search, a corresponding individual from the next group was interviewed instead. If the second-group individual could not be found, a subject from the third group was interviewed. If a third subject could not be found, the next subject in the roster of the first group was interviewed. This process continued until 67 or more subjects were interviewed in each village. We sought to interview all the members of a group that could be found. Ultimately, 89 subjects were interviewed in Chinnapalampakkam and 74 were interviewed in Thuthipet, with 68 interviewed in Mottupalayam [Appendix]. Altogether, a total of 231 subjects were interviewed.

Information gathered

We asked our study subjects questions from a questionnaire about demographics as well as chronic and acute morbidities. If they had sought care for any disease, we asked about the reasoning behind their choices. The pre-tested questionnaire was administered to all by a translator in Tamil, Hindi, or English if appropriate. Each survey was done in the presence of at least one investigator, through house-to-house visits. Participant data was collected for age, gender, level of education, type of family, living arrangement, marital status, type of home inhabited, owner of the home, current occupation, dominating source of income, general health status, and HSB.

All participants had the same information collected until the inquiry was made at the end about HSB. If respondents had sought treatment for an ailment, the reason for their choice of location and treatment was recorded. If treatment was not sought, the reason why was obtained. Qualitative answers given during interviews were written down on paper.

Data entry and analysis

After the surveys were compiled, the data were entered into a spreadsheet through a computer form, and patient answers were categorized according to the key printed on the questionnaire. The statistical software package “R” was used to analyze quantitative data. Qualitative data were analyzed by reviewing the subjects’ given reasons for not seeking treatment in the examples of illnesses where HSB was absent.

Data categorization for analysis

To make statistical analysis possible, certain questionnaire answers were grouped. Without this grouping, there would have been too many variable categories for some analysis models to work well. The groupings were as follows:

“Level of Education” was partitioned into the grade level groups: 0-4 years, 5-9 years, and 10+ years of education.

“Place of Stay” was grouped into “Living Alone (only),” “With Daughter (only),” “With Son (only), and “Other.”

“Age” was divided into years 60-69, 70-79, and 80+.

“Current occupation” was grouped into “Agricultural worker/cattle herder/farmer,” “Daily Wages,” “Retired,” “Other,” and “None.”

Results

Morbidity profile

Acute illnesses

Among the 43 participants who reported an acute illness in the past 2 weeks, 93% (40) sought treatment. 37% (16) experienced fever, 26% (11) musculoskeletal problems, 9% (4) respiratory issues, 9% (4) diarrhea, and 19% (8) “other” problems. The most common sources of treatment for acute illnesses were government-run allopathic hospitals, sought out by 44% of patients [n = 19, Table 1]. Two of our subjects suffered from more than one acute illness, one with fever and another with respiratory problems. Both sought treatment. There were no subjects with three or more acute illnesses.

Table 1.

Relationship between illness and treatment location

	Government Allopathic (n)	Private Allopathic (n)	Other (n)
Acute Illness 1
Diarrhea	1	5	-
Fever	10	6	-
Musculoskeletal	5	6	-
Others	2	4	-
Respiratory	2	1	-
Chronic Illness 1
Bronchial Asthma/COPD	3	2	0
Diabetes	11	17	3
Hypertension	15	26	1
Musculoskeletal	4	9	0
Other	10	15	1

Analysis of relationship between illness for acute and chronic disease 1 listed and where people seek treatment. Fisher’s exact test was used to test for a relationship between acute and chronic disease and where participants sought treatment. Both acute and chronic disease 1 demonstrated no relationship between the disease reported and where the subject sought treatment. P value for acute illness 1 = 0.5754, from Fisher’s exact test. P = 0.8593 for chronic illness 1, from Fisher’s exact test

Chronic illnesses

Of the 127 respondents who reported at least one chronic illness, 93% sought treatment. The 5 most common chronic illnesses were hypertension at 34% (43) of participants, diabetes at 25% (31), musculoskeletal at 11% (14), asthma/chronic obstructive pulmonary disorder (COPD) at 4% (5), and uncategorized diseases (“other”) at 18% (23). The most common sources of treatment for the first chronic illness recorded for patients, “chronic illness 1,” was government hospitals (30%) (38), the CMC – Community Health and Development (CMC-CHAD) doctor/nurse-run clinic (16%) (20), private hospital (14%) (18), “CHAD” Hospital (a small local hospital run by CMC) (12%) (15), and private clinic (11%) (14). There were 45 participants with at least two chronic illnesses and six participants with at least three chronic illnesses.

Results of Statistical Studies

No relationship found between illness category and treatment center sought

To facilitate analysis, we sorted participants’ selections of treatment centers into three categories: “Government Allopathic,” “Private Allopathic,” and “Alternative Medicine.” Using these categories, we tested for a relationship between the first acute or chronic illnesses listed for our participants and the type of treatment center sought. Fisher’s exact test revealed no relationship between acute illness 1 and where participants sought treatment (P = 0.5754) [Table 1]. We also found no relationship between chronic illness 1 and where participants sought treatment (P = 0.8593). Acute and chronic illnesses 2 and 3 could not be conveniently analyzed due to the way our data were structured.

Logistic regression revealed gender as a predictor of reported disease

We performed logistic regression on all demographic variables assessed to see if any variable predicted disease presence. We found that gender was the only statistically significant predictor of disease presence: for males, the odds of reporting disease-free status were 2.85 times greater compared to females (P = 0.00934) [Table 2].

Table 2.

Demographics predicting disease presence

	Odds ratio of disease	95% CI
(Intercept)	3.06	(0.14−69.38)
Age group
60−69 (n=122)	1
70−79 (n=74)	1.58	(0.75−3.42)
80+ (n=35)	1.29	(0.49−3.46)
Gender
Female (n=134)	1
Male (n=97)	0.35	(0.16−0.76)
Village
Chinnapalampakkam (n=89)	1
Mottupalayam (n=68)	0.92	(0.42−2.03)
Thuthipet (n=74)	1.18	(0.56−2.54)
Marital status
Married (n=119)	1
Single (n=2)	0.66	(0.01−36.38)
Widowed (n=110)	0.43	(0.18−1.00)
Type of family
Extended (n=50)	1
Joint (n=103)	0.93	(0.37−2.28)
Nuclear (n=78)	1.56	(0.12−21.02)
Place of Stay
Living alone (only) (n=80)	1
Other (n=19)	1.57	(0.13−22.08)
With daughter (only) (n=18)	0.75	(0.05−10.72)
With son (only) (n=114)	0.75	(0.06−9.07)
House
Cement sheet roof (n=32)	1
Mud walls and Thatched roof (n=17)	0.36	(0.09−1.39)
No house (n=6)	0.20	(0.02−2.63)
Terraced (n=145)	0.64	(0.22−1.73)
Tiled roof (n=31)	0.65	(0.19−2.19)
Ownership of house
Built by government (n=28)	1
Owned (n=185)	0.77	(0.25−2.16)
Rented (n=12)	1.56	(0.28−10.26)
Occupation
Agricultural worker/cattle herder/farmer (n=33)	1
Daily wages (n=15)	0.58	(0.14−2.50)
None (n=155)	1.52	(0.60−3.76)
Other (n=13)	1.49	(0.32−7.56)
Retired (n=15)	3.23	(0.70−17.49)
Education
0−4 years (n=158)	1
5−9 years (n=49)	0.80	(0.33−1.92)
10+ years (n=24)	0.82	(0.23−2.96)
Household
1 (n=38)	1
2−3 (n=76)	1.27	(0.35−4.60)
4−5 (n=47)	3.02	(0.57−16.32)
6−7 (n=49)	2.57	(0.40−16.47)
8+ (n=21)	4.12	(0.55−32.35)

Logistic Regression Model for Disease Presence. We performed logistic regression to ask if any of the variables we studied were predictors of disease presence. The only statistically significant predictor of disease presence was gender (P = 0.00934). For males, the odds of not having a disease is 2.85 times greater compared to females

93% of patients seeking treatment is a statistically significant proportion

We noticed that ~93% of patients with both chronic and acute diseases had sought treatment. We desired to confirm that this high number was truly a significant proportion and performed the two-sample test for equality of proportions, which showed there is a significant difference in the proportion of patients that sought treatment compared to those patients that did not seek treatment in both acute and chronic illness subject groups.

Relationship found between chronic illness 1 and HSB

An inspection of our data at the level of individual participants caused us to hypothesize that participants with the least common diseases were also less likely to seek treatment [Table 3]. To make analysis feasible we limited our testing to the “acute illness 1” and “chronic illness 1” categories. Further analysis with Fisher’s exact test confirmed that there is a relationship between chronic illness 1 and whether the participant sought treatment (P = 0.01118) [Table 3]. However, our analysis showed there was not a relationship between acute illness 1 and whether the participant sought treatment (P = 0.06126).

Table 3.

Comparison of Chronic Disease and Treatment Choice

Table of Chronic Illness 1
	Bronchial asthma/COPD	Diabetes	Hypertension	Musculoskeletal	Other	Total
Did not seek treatment	0	0	1	1	7	9
Did seek treatment	5	31	42	13	26	117
Table of Acute Illness 1
	Diarrhea	Fever	Musculoskeletal	Other	Respiratory	Total
Did not seek treatment	0	0	0	2	1	3
Did seek treatment	4	16	11	6	3	40

Relationship between Chronic Disease and Whether Subjects Pursue Treatment. We asked whether there was a relationship between illness type and whether subjects sought treatment. Fisher’s exact test indicates there is a relationship between chronic illness 1 and whether the subject sought treatment (P = 0.01118 from Fisher’s exact test). However, Fisher’s exact test indicates there is not a relationship between acute illness 1 and whether the subject in our study sought treatment (P = 0.06126 from Fisher’s exact Test)

Discussion

In this study, we described findings of gender as a predictor of disease and a potential relationship between chronic illness and HSB. We saw no relationship between illness category and treatment center sought, and our data suggest that 93% of patients exhibiting HSB is a statistically significant result.

93% of patients seeking treatment is a statistically significant proportion; high prevalence of HSB among subjects studied

The subjects we interviewed exhibited a high level of HSB, at 93% prevalence.[7,8,12] There are likely multiple reasons for this result. For example, in one study in China, it was found that elderly subjects avoided care to prevent the crippling cost to the family.[13] It was also found that rural Chinese elderly only considered themselves in need of healthcare when they were unable to perform the work role they fulfilled for the family, usually farm work and/or child care.[13] By contrast, previous work by Sharma and colleagues in North India revealed that geographic access to care was a barrier for elderly in the mountainous Shimla Hills region, where musculoskeletal problems were the most-reported illness at 55% of participants.[3] However, chronic illnesses in our study near Vellore were most commonly hypertension (34%) (43), with musculoskeletal problems much lower (26% among acute patients) (11). Chronic musculoskeletal complaints were even less common in our study near Vellore at 11% (14). This large presence of HSB in Vellore could be a result of selection bias due to our use of a roster maintained by CMC. This roster may not be a perfect representation of the local community and could be enriched with individuals who are already engaged with the local healthcare system.

It is interesting that musculoskeletal problems are not #1 in the Vellore environment as they were in the Shimla Hills study, and further work is needed in this area.[3] There are significant differences in geography between the two regions, perhaps the flat area around Vellore is easier on joints over a lifetime. Indeed, previous work by Srinivas and colleagues did find similar levels of HSB in the topologically similar state of Kerala,[15] with a morbidity profile also similar to our Vellore data.

No relationship found between illness category and treatment center sought

Our patients sought care at a broad variety of facilities. Statistical analysis with Fisher’s exact test showed that the category of illness was not associated with HSB directed at one type of facility [Table 1]. While the reasons for this are unclear, it could be that patients simply do not care where they go for treatment or it could be that the difference was not detected due to the power of our study.

Logistic regression revealed gender as a predictor of reported disease

Logistic regression analysis showed that for our data, the only statistically significant predictor of disease presence was gender [Table 2]. For males, the odds of not having a disease are 2.85 times greater compared to females [Table 2]. Prior work in Shimla Hills similarly revealed a significant gender difference, although in a mean number of morbidities mentioned by the participants; 2.06 in males vs. 2.58 in females.[3] While it is possible that the difference in our study is due to gender being a true risk factor for health discrepancies in rural India, a brief discussion of alternative explanations can be helpful. The data could be explained by a difference between males and females in disease reporting, or by a survivorship bias; females tend to outlive males around the world, and an older group tends to have greater morbidities. Sharma and colleagues suggested that the discrepancy they found could have been due to differences in employment, exercise, or widowhood.[3] More work is needed in this area.

Relationship found between chronic illness #1 and HSB

We found this result interesting as it appears as if less-common diseases predispose a participant against HSB [Table 3]. A visual inspection of our data in table of chronic illness 1 caused us to speculate that diabetes, hypertension, and musculoskeletal complaints may trigger HSB, while the “other” category, including sensory-related problems, does not [Table 3]. It may be that elderly subjects, when faced with certain disorders, conclude that nothing more can be done and choose not to pursue care. Indeed, two of the patients in this category had eye complaints; it is easy to imagine a patient with cataracts going without treatment when they do not realize it is available. Poor vision can be associated with increased fall risk and the associated morbidities.[16] This may represent opportunities to provide care to patients who do not realize that their condition is treatable.

These data suggest that public health efforts near Vellore could find even more success by focusing on patients with less-common diseases and working to establish the underlying reasons behind differences in male/female disease prevalence near Vellore.

Strengths and limitations

This study is bolstered by the wide variety of data on a specific group of patients, data which can support the work of local healthcare experts. In addition, our method of actively finding participants in the field helps reduce the sampling bias of more passive data collection measures, such as inpatient surveys.

Our study was not without limitations. These include the possibility that healthier, more active elderly being out of the village during survey could have biased the study towards a population with higher morbidity levels. The process of selecting study participants could be improved by selecting subjects randomly, and limiting the patients selected from various villages to equal proportions. Our data could also have been affected by our use of a patient roster maintained by CMC. It is possible that there were patients that eschew contact with CMC workers for unknown reasons and therefore were missed by our CMC-connected team.

Our use of two interview teams could have caused differences in response from participants; there may have been biases in how questions were asked, or the way answers were translated back to paper, as two examples. Our sample size was relatively small and covered a limited geographic area, so the results should not be extrapolated to a wide variety of populations. The possibility of unanticipated confounding variables cannot be eliminated with an observational study of this type. Finally, since Vellore elderly exhibited such high HSB prevalence, the sampled population of non-health-seeking elderly is small. This limits the power of our study for certain types of analysis.

Controversies and future research directions

Future research should survey a large number of non-health-seeking elderly patients as to why they chose not to pursue healthcare. It should include a broader sample of villages over a wider geographic area and include the elderly living in urban Vellore itself. This would allow comparison with other studies that evaluated differences in care between urban and rural areas. Additionally, using a non-institutionally maintained source of study participants may decrease bias related to HSB and participant access to healthcare.

Conflicts of interest

There are no conflicts of interest.

Appendix

Descriptive Statistics

Demographics

Many study participants had received zero years of school. Years of schooling trended downward with increasing age. Education was rare above the age of 80. Most rural elderly around Vellore depended on pension or personal earnings for their income. Most study participants lived alone or with a son. None of our subjects were divorced.

Wealth and Living Situation

Many of our participants were experiencing poverty. The most common primary source of income was the Old Age Pension, at about 50% of respondents. Personal earnings (15%), dependence on children (14%), and retirement pensions (27%) were also common as primary sources of income. The remaining 9% of the elderly interviewed reported other various sources of income.

Education

We found that most of our sample population, 59%, had no formal schooling, and that some were highly educated as well.

Place of Stay

About half of the elderly (50%) we interviewed reported living with a son, while 35% lived independently. Roughly 8% of subjects lived with their daughter, and the remainder had some other living situation.

Type of Home

Home types were categorized as follows: “cement sheet roof,” “mud walls with thatched roof,” “terraced,” and “tiled roof.” Most subjects (63%) lived in homes with terraces (flat roof surfaces that could be walked on), 13% lived in tiled roof homes and 14% lived in cement sheet roof homes. 7% of interviewees lived in mud homes with a thatched roof, and 3% were homeless. 80% of the homes were owned, 12% were built by the government, 5% rented, and the remaining 3% of subjects had some other housing situation, such as homelessness.

Village

Eighty nine of our subjects lived in Chinnapalampakkam (~39%), 68 lived in Mottupalayam (~29%), and 74 lived in Thuthipet (32%).

Family Environment and Personal Characteristics

Roughly 45% of our respondents lived in “joint” families, meaning an individual living with or without immediate family as well as any member of a spouse’s family. 22% were in “extended” family situations; an individual living with or without immediate family as well as any other blood relatives. The remaining 34% of participants were in “nuclear” families, with the participant living with only immediate family.

Marital Status

Among our study subjects, roughly half the population (about 52%) were still married and about 48% were widowed. Two subjects (~1%) had never married. No study subjects reported being divorced.

Age

The mean age of our sample population was ~70 with a standard deviation of 7.92. The oldest subject we interviewed was 96, while the youngest was 60.

Gender

Over half the sampled population was female, roughly 58%, while ~42% was male.

Religion

224 of our subjects, about 97%, identified as “Hindu.” 5 (about 2%) identified as “Christian,” and 2 (about 1%) identified as “Muslim.”

Household Size

The average number of people in each household was 4.03, with a maximum of 15 and a minimum of 1. The standard deviation was 2.54.

Types of Family Living Arrangements

In the survey, the participant’s “type of family” was categorized as “nuclear,” “joint,” or “extended.” “Joint” consisted of an individual living with or without immediate family as well as any member of a spouse’s family. “Extended” included an individual living with or without immediate family as well as any other blood relatives. “Nuclear” represented an individual living with only immediate family.

List of Abbreviations

Abbreviation	Definition
CMC	Christian Medical College

Acknowledgements

Name	Role

Contribution Details

Enter the role of contributors in the first column and names of the contributors in the columns 2, 3, and so on.

Role (Concepts, Design, Definition of intellectual content, investigation, manuscript writing, etc.)	Mark Flage	Dip Shukla	Ishan Sahu	Chehan Herath	Paige Williams	Vinod Abraham	Charlotte Bolch
Investigation plan	X					X
Data collection	X	X	X	X	X
Statistical analysis	X	X				X	X
Data Presentation	X						X
Manuscript writing	X	X	X	X		X
Logistical Support	X			X		X
Host institution liaison, Advisor						X

Ethical policy and Institutional Review board statement:

Our study was carried out under the approval of the Institutional Review Board of Christian Medical College, Vellore, and in accordance with the Helsinski Declaration of 1975, as revised in 2000.

Patient declaration of consent statement:

Signed informed consent was acquired from each interviewee prior to administration of each questionnaire and interview. Interviewees who could not write signed with a thumbprint instead.

Data Availability statement: The data set used in the current study is available (tick the appropriate option and fill the information)

□ repository name

_________________________________________________________________________

□ name of the public domain resources

_________________________________________________________________________

□ data availability within the article or its supplementary materials

_________________________________________________________________________

□ available on request from (contact name/email id)

___Mark Flage, mark.flage@midwestern.edu_____________________________________

□ dataset can be made available after embargo period due to commercial restrictions

_________________________________________________________________________

Reporting guidelines:

Manuscript adheres to appropriate reporting guidelines for observational studies.

Fill the checklist given below:

Reporting guidelines for Original Research Articles (Case-control, Cohort and Cross-sectional studies): STROBE (2007).

	Item No	Recommendation	Yes/No
Title and abstract	1	(a) Indicate the study’s design with a commonly used term in the title or the abstract	Yes
		(b) Provide in the abstract an informative and balanced summary of what was done and what was found. Structured abstract: Aims & Objectives, Materials & Methods, Results, Conclusion Format to be consistent	Yes
Introduction
Background/rationale	2	Explain the scientific background and rationale for the investigation being reported	Yes
Objectives	3a	State specific objectives, including any prespecified hypotheses. The research objective should not be biased.	Yes
	3b	Statements to be appropriately cited	Yes
Methods – Structured methods section (with subheadings) is preferred
Study design	4a	Present key elements of study design early in the paper (cross-sectional/cohort/case-control)	Yes
	4b	Is the study design robust and well-justified?	Yes
Setting	5a	Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection	Yes
	5b #	Mention the details of the supplier/manufacturer of the equipment/materials (e.g., chemicals) used in the study	N/A
	5c #	Mention the details of the drugs (manufacturer, dosage, dilution, frequency and route of administration, monitoring equipment) used in the study	N/A
	5d #	Mention the details about the cell lines (names and where it was obtained from)	N/A
	5e #	Mention the details of plant sample collection (location, time period, validation of the specimen, Institution where the specimen is submitted and the voucher specimen number)	N/A
Participants	6	(a) Cohort study—Give the eligibility criteria (inclusion/exclusion), and the sources and methods of selection of participants. Describe methods of follow-up	N/A
		Case-control study—Give the eligibility criteria (inclusion/exclusion), and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls	N/A
		Cross-sectional study—Give the eligibility criteria (inclusion/exclusion), and the sources and methods of selection of participants	Yes
		(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed	N/A
		Case-control study—For matched studies, give matching criteria and the number of controls per case	N/A
Variables	7a	Clearly define all outcomes (primary and secondary), exposures, predictors, potential confounders, and effect modifiers.	Yes
	7b	Give diagnostic criteria, if applicable	N/A
Data sources/measurement	8*	For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group	Yes
Bias	9	Describe any efforts to address potential sources of bias	Yes
Study size	10	Explain how the study size (sample size) was arrived at	Yes
Quantitative variables	11	Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why	Yes
Statistical methods (a separate heading needed)	12	(a) Describe all statistical methods, including those used to control for confounding	Yes
		(b) Describe any methods used to examine subgroups and interactions	Yes
		(c) Explain how missing data were addressed	N/A
		(d) Cohort study—If applicable, explain how loss to follow-up was addressed Case-control study—If applicable, explain how matching of cases and controls was addressed Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy	Yes
		(e) Describe any sensitivity analyses	N/A
Results
Participants	13*	(a) Report numbers of individuals at each stage of study—e.g., numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analyzed	Yes
		(b) Give reasons for non-participation at each stage	Yes
		(c) Consider use of a flow diagram	Yes
Descriptive data	14*	(a) Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders	Yes
		(b) Indicate number of participants with missing data for each variable of interest	N/A
		(c) Cohort study—Summarise follow-up time (e.g., average and total amount)	N/A
Outcome data	15*	Cohort study—Report numbers of outcome events or summary measures over time	N/A
		Case-control study—Report numbers in each exposure category, or summary measures of exposure	N/A
		Cross-sectional study—Report numbers of outcome events or summary measures	Yes
Main results	16	(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence interval). Make clear which confounders were adjusted for and why they were included	Yes
		(b) Report category boundaries when continuous variables were categorized	Yes
		(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period	N/A
Other analyses	17	Report other analyses done—e.g., analyses of subgroups and interactions, and sensitivity analyses	Yes
Presentation	18a	Tables and graphs properly depicted with no repetition of the data in the text	Yes
	18b	Annotation/footnotes to be mentioned appropriately	Yes
	18c	Abbreviations to be defined in the footnotes	Yes
Discussion
Key results	19	Summarise key results with reference to study objectives	Yes
Limitations	20	Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias	Yes
Interpretation	21	Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence	Yes
Generalisability	22	Discuss the generalisability (external validity) of the study results	Yes
Citations	23a	The statements should be adequately cited	Yes
	23b	Recent citations (last 5 years) to be cited in a greater proportion	Yes
Other information
Funding	24a	Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based	Yes
	24b	Mention the grant number	N/A
Ethical approval and Patient Consent	25a	Mention the IRB approval and the approval number (for animal and human subjects)	Yes
	25b	Mention if the study has been conducted in accordance with the ethical principles mentioned in the Declaration of Helsinski (2013)	Yes
	25c	Mention if the patients have consented to participate in the study. To mention if consent has been waived/exempted by IRB	Yes
Conflict of Interest	26	Mention the financial, commercial, legal, or professional relationship of the author (or the author’s employer) with sponsors/organizations that could potentially influence the research.	Yes
Language	27	The language should be understandable without grammatical errors that hinders the readability	Yes

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies. ^#Give information depending on the study sample

Funding Statement

University of Minnesota College of Biological Sciences $6,000 grant.