Epidemiology of brucellosis in cattle and dairy farmers of rural Ludhiana, Punjab.

Brucellosis is a zoonotic disease imposing significant impacts on livestock production and public health worldwide. India is the world's leading milk producer and Punjab is the state which produces the most cattle and buffalo milk per capita. The aim of this study was to investigate the epidemiology of bovine brucellosis to provide evidence for control of the disease in Punjab State, India. A cross-sectional study of dairy farms was conducted in humans and livestock in rural Ludhiana district using a multi-stage sampling strategy. The study suggests that brucellosis is endemic at high levels in cattle and buffalo in the study area with 15.1% of large ruminants testing seropositive and approximately a third of dairy farms having at least one animal test seropositive. In total, 9.7% of those in direct contact with livestock tested seropositive for Brucella spp. Persons that assisted with calving and/or abortion within the last year on a farm with seronegative livestock and people which did not assist with calving/abortion had 0.35 (95% CI: 0.17 to 7.1) and 0.21 (0.09 to 0.46) times the odds of testing seropositive compared to persons assisting with calving/abortion in a seropositive farm, respectively. The study demonstrated that persons in direct contact with cattle and buffalo in the study area have high risk of exposure to Brucella spp. Control of the disease in livestock is likely to result in benefits to both animal and public health sectors.

Introduction

India is currently the world’s leading milk producer; this study was conducted in Punjab which has the highest per capita milk production of all the Indian States (937g per person per day) [1]. Despite the development of cooperatives and increases in the number of organised dairy farms, a study in 2011 found the majority of milk in Punjab State was produced by rural smallholders and flowed through informal channels [2]. Endemic and emerging livestock diseases pose a threat to the milk industry due to reduced productivity of livestock (direct impact) and trade barriers (indirect). In addition, milkborne pathogens pose a threat to consumer health. Bovine brucellosis is a bacterial zoonosis of global importance due to its impact on livestock production, trade and human health [3,4]. In livestock, the disease reportedly causes economic losses to farmers through abortions and subsequently decreased milk yield [5,6].

Brucellosis is considered endemic in all states of India and recent increases in the incidence have been attributed to the intensification in the dairy industry resulting in increased cattle numbers and density as well as trade and livestock movement (Gwida, Al Dahouk et al. 2010, Kumar 2010). Herd-level and individual seroprevalence estimates in cattle and buffalo in Punjab have previously been estimated to be as high as 65.5% and 21.4%, respectively [7,8]. However, many of the previous studies have not used probabilistic sampling therefore the estimates may be biased. One study where sampling was unbiased was conducted over 10-years ago and reported a lower animal-level seroprevalence estimate (11.2%) [9].

Brucellosis is often cited as one of the world’s most common bacterial zoonosis and is transmitted to humans via the oral, respiratory, or conjunctival routes either via direct contact or consumption of unpasteurised milk and dairy products [10,11]. Therefore, Incidence of the disease in humans relates to the frequency of brucellosis in the local livestock population and it is an occupational zoonosis for farmers and veterinarians [12,13]. The disease is under-reported in many parts of the world, often misdiagnosed as malaria or typhoid due to non-specific clinical signs. According to the ‘WHO estimates of the global burden of foodborne diseases: foodborne disease burden epidemiology reference group 2007–2015’ there are 832,633 cases of brucellosis per year (95% Uncertainty Interval (UI): 337 929 to 19 560 440) [3]. However, in a systematic review of scientific studies estimating the frequency of human brucellosis (1990 to 2010) commissioned as part of this project no studies from the Asia region passed the validity assessment [14]. Given that Asia contains approximately 60% of the world’s population, with India comprising around 17%, estimates of the frequency of human brucellosis here are critical to assess the global burden of the disease [15]. Knowledge of the relative importance of the different transmission routes can aid the design of surveillance systems and awareness campaigns targeting human brucellosis. The WHO estimated that in ‘South East Asia Region D’, which contains India, the proportion of brucellosis cases via the foodborne route and direct contact with livestock is 0.45 (95% UI: 0.07 to 0.70) and 0.50 (95% UI: 0.22 to 0.82), respectively [3]. However, this estimate was based on the opinion of only seven experts and may have low precision, as reflected in the extremely wide uncertainty estimates. High quality data on the burden of human brucellosis in India are scarce. However, available evidence suggests humans are exposed via occupational contact in Punjab, with veterinarians and farm workers at high risk [12].

The objectives of this study were to estimate individual and farm-level seroprevalence of Brucella spp. in cattle and buffalo in dairy farms in Ludhiana district of Punjab and identify risk factors associated with Brucella spp. seropositivity. In addition, seroprevalence of Brucella spp. in persons in direct contact with these livestock was estimated and risk factor analysis performed.

Methods

Ethics statement

Ethical approval was obtained from the ethics committees at Guru Angad Dev Veterinary and Animal Sciences University, and the Post-Graduate Institute of Medical Education and Research (PGIMER), India, the London School of Hygiene and Tropical Medicine (LSHTM) and the Royal Veterinary College (RVC) in the UK. Informed, written consent for interviews and blood samples was obtained from all participants, or their legal guardian for minors.

Study population and sampling

Between June 2015 and September 2017, a cross-sectional study of dairy farms was conducted using a multi-stage sampling strategy. For the purpose of the study, a ‘dairy farm’ was defined as any farm keeping at least four cows and/or female buffalo of reproductive age. Herds were classified as positive if at least one animal tested seropositive. Assuming an average of four lactating animals sampled per herd and an individual test specificity of 99% [16-19], herd specificity (HSp) was estimated to be 96%. Values of Herd Sensitivity (HSe) (likelihood of correctly classifying a positive herd) varied depending on herd size, number of lactating animals sampled and within-farm prevalence. Different combinations of these values were explored in order to achieve a desired HSe of 70% [20]. Using a design prevalence of 10% (the minimum within-farm prevalence to detect), sampling a total of nine animals per herd would result in HSe values above 70% given the values presented in Table 1. A sample size of 384 herds would allow herd-level prevalence to be estimated with a 6% precision and 95% confidence. Different precision values were trialled and 6% was selected as a trade-off between accuracy of the estimated and number of herds that could be sampled given available resources. This sample size was multiplied by an assumed design effect of 1.07, calculated from previous surveys, to account for correlation of herds within villages to give a final sampling target of 411 dairy farms.

Table 1

Values used to calculate sample size in order to estimate farm-level prevalence.

Variable	Value	Ref
Herd size	5–20	Pilot study
No. lactating animals sampled	1–9	Inputted value
Design prevalence (min)	0.2	Inputted value
Individual test sensitivity (milk ELISA)	0.98	[16–19]
Individual test specificity (milk ELISA)	0.99
Calculated herd sensitivity (min)	0.72	http://epitools.ausvet.com.au/content.php?page=HerdSens4
Calculated herd specificity	0.95
Expected herd-prevalence	0.25	Expert opinion
Desired Precision (confidence)	0.06 (0.95)	Inputted value
No. farms in study area	10,000	Calculated from Census of India. 2011
Calculated sample size–unadjusted for clustering	384	Purposefully-designed spreadsheet
Design effect	1.07	Assumed from previous studies
Total herds–adjusted for clustering	411

In the first stage of sampling, four out of seven Community Development Blocks (CD block) (administrative division) of Ludhiana district were selected to restrict location for practical purposes. East Ludhiana has the largest human population [21], therefore this CD block was included and every other CD block was sampled working anti-clockwise in a systematic manner (Jagraon, Payal and Samrala). Within these CD blocks, 60 villages were randomly selected using sampling probability proportional to human size [21]. In order to meet the required sample size, eight dairy farms per village were selected using simple random sampling. In an average village, eight farms were estimated to comprise around 10% of the total dairy farms [21,22]. Lists obtained from the designated veterinary officer or pharmacist of each village were used as a sampling frame. If eight of fewer eligible farms were present in the study area then all farms were approached for inclusion in the study. If the dairy farm had one to nine lactating animals then all lactating animals were sampled where possible. If the dairy farm had more than nine animals, a systematic sample of nine animals was taken. Within selected farms, all people present involved with livestock husbandry (mostly farm owners, farm workers (employees) and family members) were offered serological screening for Brucella spp.

Sample and data collection

From selected livestock, milk samples were collected from each quarter into a single 50ml polyethylene tube, after thoroughly cleaning and drying the teats. The samples were placed immediately into a cool box and refrigerated at 4°C within five hours. Samples were aliquoted into Eppendorf tubes and these were then frozen at -20°C for up to 6 months before de-frosting at room temperature for testing. Two 4ml venous blood samples were collected from each person selected using vacutainers one containing a clot activator and one containing EDTA. These were transported in a cool box to the School of Public Health and Zoonosis (GADVASU), centrifuged on the day of collection and stored at -20°C.

Data regarding potential factors associated with Brucella spp. seropositivity in livestock were collected during face-to-face interviews using questionnaires pre-piloted in two villages (Questionnaires available on request). Causal diagrams to conceptualise potential pathways for farms, individual animals and persons in direct contact with livestock in the dairy farms being exposed to Brucella spp. were created (Fig 1). These diagrams were used to design the questionnaires and inform variable selection for statistical analysis. The first questionnaire was conducted with the farm owner or manager and collected data on potential farm-level risk factors including management practices and contacts with other livestock. The second, also conducted with the owner or manager, contained questions regarding individual characteristics of animals sampled. A third questionnaire was administered to persons in direct contact with livestock who were being tested for Brucella spp. and collected information of putative risk factors for human infection (livestock contact and dairy product) consumption. All interviews were conducted in Punjabi by trained research fellows using paper forms with subjects anonymised and given a unique ID number.

Fig 1

Conceptual diagram depicting the variables hypothesised to be associated with seropositivity in i) dairy farms ii) livestock and iii) persons in direct contact with livestock in the dairy farms.

Data on these variables was gathered in the questionnaires and used in the statistical analysis.

Serological testing

Livestock samples

Cattle and buffalo milk samples were screened for Brucella spp. using a commercial indirect milk ELISA developed by the OIE brucellosis reference laboratory at the Animal and Plant Health Agency (APHA), UK (BRUCELISA). The assay detects IgG antibodies to smooth Brucella strains in bovine milk samples. The BRUCELISA testing was carried out as per the manufacturer’s instructions. A positive/negative cut-off was calculated as 50% of the mean of the optical density (OD) of the medium positive control wells. The medium positive was used so that the positive/negative cut-off can be defined more robustly as it is closer on the dose-response curve to that cut-off than a strong positive control. Thus, potential inter-plate variation due to any fluctuation in the shape of the dose-response curve is minimised (Senior Scientist, APHA). Any test sample giving an OD equal to or above this was classified as positive. All milk samples which tested seropositive and a proportion of randomly selected negative samples were retested using the same procedure. All testing was performed in the Department of Microbiology, GADVASU.

Human samples

Human sera were tested using the Rose-Bengal test (RBT) for the detection of antibodies against Brucella spp. The antigen for this was supplied by the Punjab Vaccine Institute in India. Briefly, a sample of serum (0.05 ml) was mixed with 0.05 ml of antigen on a microscope slide to produce a zone approximately 2 cm in diameter. The mixture was agitated gently for four minutes at room temperature and observed for agglutination. If a visible reaction was observed, it was considered positive. A subset of samples (10 negatives, 7 positives) were also screened with antigen from the APHA for quality assurance. These samples showed perfect correlation. All serum samples were then tested for IgG antibodies for Brucella spp. using commercial ELISA kits supplied by DEMEDITEC Diagnostics GmbH, Kiel, Germany. As per the manufacturer’s standard kit protocol, optical densities (ODs) were read at 450nm. For each plate, samples were interpreted as positive if ODs were >20% over the manufacturer’s cut-off standard, negative as <20% under the cut-off, and inconclusive if in-between. All inconclusive ELISA samples were classified as negative for the purposes of this study. Both serological tests detect antibodies against the smooth lipopolysaccharide (sLPS) shared between the smooth strains of Brucella spp. (B. abortus, B. melitensis and B. suis). The Demeditec IgG iELISA (used in the serological testing of humans for prevalence of infection) was validated by the manufacturer though parallel testing against a CE-marked equivalent ELISA product. This study gave a value of 100% specificity (n = 88) and 100% sensitivity (n = 9).

All inconclusive ELISA samples were classified as negative. Additionally, all samples testing seropositive to the ELISAs or RBT were tested using the Standard Tube Agglutination Test (SAT) for diagnosis of active disease that may require treatment. Persons with a SAT titre of 1/160 International Units (IU) or higher received a phone-call from the principal investigator at GADVASU and were referred to a clinician at GADVASU who works with farmers and veterinarians to diagnose and treat brucellosis in the study area for further follow-up if they agreed.

Statistical analysis

Seropositive samples from livestock were retested with the same BRUCELISA test and livestock were classified as positive if two seropositive test results were obtained. People were classified as seropositive if they tested positive to either RBT or IgG ELISA. Farms were classified as positive if at least one animal within the farm was classified seropositive for Brucella spp. after serial testing with the iELISA.

Univariate logistic regression analysis with random effects was used to provide crude estimates for the associations between candidate risk factors and Brucella spp. seropositivity in the three epidemiological units under study. Model outcomes were: i) individual animals testing seropositive (with farm and village included as a random effect), ii) dairy farms classified as seropositive (with village included as a random effect) and iii) persons in direct contact with livestock (with village included as a random effect). Farm was not included as a random effect for the model investigating risk factors in people in direct contact with livestock as only one person was tested in 75.7% of farms. Odds ratios (ORs) were calculated, comparing the odds of testing seropositive for Brucella spp. between each level of the variable and the baseline category Variables where one or more categories were associated with Brucella spp. seropositivity with a P-value ≤ 0.2 were retained for further multivariate analysis.

Generalised linear mixed models (GLMM) were then used to identify risk factors for the three study units testing seropositive, controlling for potential confounders. Age was included as an a priori confounder in both the individual animal and human models. A manual forward step-wise procedure was used to build the models with variables with the lowest P-values entered into the model first. Upon addition of each variable, likelihood ratio tests were performed comparing the fit of the new model with the previous and to compare whether model fit was better when linear variables were kept as linear exposures or categorised. Variables were retained if there was significant evidence (P-value ≤ 0.0.5) against the null hypothesis that the simpler model was better and/or their addition changed (+/-) the OR for the association between another variable and Brucella seropositivity by ≥ 10% (possible confounding). Biologically plausible interaction terms were also investigated (Fig 1). Each model was checked for multicollinearity by calculating the Variance Inflation Factor (VIF) using the R package ‘performance’; VIF values greater than 10 were considered non-tolerable. Odds ratios and 95% confidence intervals were reported. Intra-cluster correlation coefficients (ICCs) were also calculated from the final multivariate models. All analyses were performed in R version 3.5.2, GLMM was done using the package lme4.

Results

Livestock

The cross-sectional study included 413 farms in 58 villages (two of the villages did not have farms meeting the criteria for inclusion–at least four adult cattle and/or buffalo). In addition to producing milk for home consumption, the majority of farms sold milk to milk vendors in the informal sector (85.3%), whilst 44 (10.9%) sold to cooperatives, 6 (1.5%) sold to private companies and 9 (2.2%) kept all their milk for home consumption or sold to neighbours. Farm size ranged between 4 and 105 (Median: 10). Of the 1802 milk sample collected from cattle and buffalo, 312 initially gave a positive result and 272 produced two consecutive positive results using the commercial IgG ELISA (BRUCELISA). Therefore, individual livestock seroprevalence was estimated to be 15.1% (95% CI: 13.5 to 16.8). Overall, 96 (11.9%) buffalo and 174 (17.9%) milk samples from cows tested positive. A total of 136 farms had at least one animal that tested seropositive, therefore, farm-level seroprevalence was estimated to be 32.9% (95% CI: 28.6% to 37.6%).

Dairy farms

Only 10 (2.4%) farms kept goats and two of these had at least one large ruminant tested seropositive for Brucella spp. The results of the analysis for variables associated with farms testing seropositive are presented in Tables 2 and 3 (univariate).

Table 2

Farm-level univariate analysis for associations between farm demographics and at least one animal in the herd testing seropositive using logistic regression with village included as a random effect.

Variable	Frequency (%)	No. Pos (%)	Odds ratio	P–value
CD block
Jagraon	122 (29.5%)	29 (23.8%)	1	-
East Ludhiana	90 (21.8%)	24 (26.7%)	2.20 (1.26 to 3.89)	0.006
Payal	113 (27.4%)	46 (40.7%)	2.33 (1.29 to 4.24)	0.005
Samrala	88 (21.3%)	37 (42.0%)	1.17 (0.62 to 2.18)	0.63
Total	413	136
Herd type
Mixed	243 (58.8%)	69 (28.4%)	1	-
Buffalo only	80 (19.4%)	24 (30.0%)	1.08 (0.61 to 1.86)	0.783
Cows only	90 (21.8%)	43 (47.8%)	2.31 (1.40 to 3.81)	0.001
Total	413	136
Total cows
None	107 (25.9%)	32 (29.9%)	1	-
1 or 3	129 (31.2%)	35 (27.1%)	0.87 (0.49 to 1.54)	0.638
4 to 6	96 (23.2%)	29 (30.2%)	1.01 (0.55 to 1.85)	0.963
More than 6	81 (19.6%)	40 (49.4%)	2.29 (1.26 to 4.20)	0.007
Total	413	136
Adult female buffalo
None	108	47	1
1 or 5	204	58	0.52 (0.32 to 0.84)	0.008
More than 6	101	31	0.57 (0.32 to 1.01)	0.056
Total	413	136
Total herd size
1 to 7	105	28	1	-
8 to 10	131	37	1.08 (0.61 to 1.94)	0.787
10 to 15	106	36	1.41 (0.79 to 2.57)	0.250
More than 15	71	35	2.67 (1.42 to 5.09)	0.002
Total	413	136

Table 3

Herd-level univariate analysis for associations between herd management and at least one animal in the herd testing seropositive, using logistic regression with village included as a random effect.

Variable	Frequency (%)	No. Pos (%)	Odds ratio	P–value
Purchased new livestock
No	369 (89.3%)	124 (33.6%)	1	-
Yes	44 (10.7%)	12 (27.3%)	0.74 (0.36 to 1.45)	0.400
Total	413	136
Sold livestock
No	363 (90.3%)	127 (34.0%)	1	-
Yes	40 (9.7%)	9 (22.5%)	0.56 (0.25 to 1.17)	0.144
Total	413	136
Insemination cows
AI	278 (88.3%)	94 (33.8%)	1	-
Natural	37 (11.7%)	15 (40.5%)	1.33 (0.65 to 2.67)	0.420
Total	315	109
Insemination buffalo
AI	223 (74.1%)	64 (28.7%)	1	-
Natural	78 (25.9%)	24 (30.8%)	1.10 (0.62 to 1.92)	0.729
Total	301	88
Natural service in either species
No	164 (67.5%)	49 (29.9%)	1	-
Yes	79 (32.5%)	25 (31.6%)	1.09 (0.60 to 1.93)	0.779
Total	243	74
Disinfect after calving
Always	310 (77.3%)	115 (37.1%)	1	-
Not always	91 (22.7%)	18 (19.8%)	0.42 (0.23 to 0.72)	0.002
Total	401	133
Separate at calving
Always	248 (62.5%)	76 (30.6%)	1
Not always	149 (35.7%)	56 (37.6%)	1.36 (0.89 to 2.98)	0.156
Total	397	132

In the final multivariate model, farms from Payal and East Ludhiana were more likely to be seropositive compared to Jagraon (Table 4). In addition, the odds of farms testing seropositive increased with the number of adult female cows on the farm. Unexpectedly, farms which reported “never” or “sometimes” disinfecting after calving had 0.43 (95% CI: 0.21 to 0.81) times the odds of testing seropositive for Brucella spp. The adjusted ICC of the farm model was 0.11, suggesting that 11% of the variance can be accounted for by the clustering of people within village.

Table 4

Herd-level multivariate GLMM for associations between farm-level risk factors farms testing seropositive for Brucella spp. using logistic regression with village included as a random effect.

Variable	Odds ratio (95% CI)	P-value
CD blockJagraonEast LudhianaPayalSamrala	12.62 (1.29 to 5.84)2.69 (1.19 to 5.91)1.19 (0.53 to 2.74)	-0.0080.0150.671
SpeciesNumber of cows	1.08 (1.03 to 1.15)	0.006
Disinfect at calvingAlwaysNot always	10.42 (0.21 to 0.81)	0.01

Individual livestock

The results for the univariate analysis for individual livestock and farms testing seropositive for Brucella spp. is presented in S1 Table. Only 24 (1.3%) animals in four farms were vaccinated against Brucella spp. These animals were vaccinated using the reduced dose S19 vaccine via the conjunctival route. CD block, species, breed, origin and whether or not the animal was vaccinated against Brucella spp. were taken forward to the multivariate analysis. Only CD block and species was retained in the final model with age included as an a priori confounder (Table 5). Cows had 1.75 (95% CI: 1.29 to 2.43) times the odds of testing seropositive for the disease compared to buffaloes. Species and breed exhibited collinearity so only one was retained in the model. It was decided to keep species as there was some heterogeneity with how breed was recorded and some categories had few observations. The adjusted ICC for individual livestock was 0.164 for village and 0.005 for farm.

Table 5

Individual livestock multivariate GLMM for associations between individual level variables and cattle testing seropositive using logistic regression with village and farm included as a random effect.

Variable	Odds ratio (95% CI)	P-value
CD blockJagraonEast LudhianaPayalSamrala	-2.48 (1.20 to 5.27)3.42 (1.59 to 7.49)1.67 (0.78 to 3.66)	-0.0130.0010.174
SpeciesBuffaloCow	-1.75 (1.29 to 2.43)	-<0.001
Age	1.05 (0.98 to 1.13)	0.165

Occupationally exposed survey

A total of 585 individuals which had direct contact with livestock in 360 (87%) of the studied farms gave informed consent to be screened for Brucella spp. and interviewed. The median number of persons sampled per farm was 1 (range 1 to 8). Based on a classification of IgG ELISA or RBT positive, seroprevalence (unadjusted) was estimated to be 9.7% (95% CI: 7.4% to 12.3%) with 57 people testing seropositive. Only 36 people (6.6%) had heard of brucellosis, with 6 (11.0%) of these testing seropositive. A total of 15 people thought they had suffered from brucellosis and 6 (40%) of these tested seropositive for the disease. Table 6 shows the comparison between results of the IgG ELISA and RBT. Only one sample was positive by RBT but negative by IgG ELISA, whereas 40 samples were positive by IgG ELISA and negative by RBT. All samples which were inconclusive by IgG ELISA were classified as negative. Of the 57 people that were classified as seropositive, 13 (22.8%) produced SAT titres of 1/160 or higher (Table 7). All of these were RBT positive, however, only 2 mentioned symptoms in the last 12 months. See S2, S3 and S4 Tables for further description of the study population.

Table 6

Comparison of IgG ELISA and RBT results in persons in direct contact with large ruminants.

		RBT		Total
		-	+
IgG ELSA	-	474	1	475
	inc	54	0	54
	+	40	16	56
Total		568	17	585

Table 7

SAT results of 57 persons in direct contact with large ruminants, classified as seropositive for Brucella spp.

Result	N	RBT +	IgG ELISA +	Symptoms?
≥1/80	16 (28.1%)	15	16	2 (1 fever, 1 joint pain)
≥1/160	15 (26.3%)	14	15	2 (1 fever, 1 joint pain)
≥1/320	13 (22.8%)	13	13	2 (1 fever, 1 joint pain)

CD block, gender, role on the farm, level of schooling and activities in the farm (milking; assisting with calving; assisting with abortion) were taken forward to the multivariate analysis. The variable ‘how many abortions have you assisted with in the last 12 months’ used in the statistical analysis (categorised as 0 or ‘at least 1’) as opposed to the categorical variable ‘do you assist with abortions on the farm’: yes (within the last 12 months); yes in the past; no. These variables were colinear, however, there was some misinterpretation in the latter question; some people reported assisting with abortion if it would be required as part of their role, even if they hadn’t actually performed that task. No factors related to dairy product consumption were taken through to the multivariate analysis. Gender was not included as a potential confounder as there was collinearity between gender and the exposures due to gender roles within the farm. The ICC of the intercept-only model was 0.214, suggesting that 21.4% of the variation can be accounted for by the clustering of people within village. The results of the multivariate model are presented in Table 8. Assisting with calving and assisting with abortion were combined in to a new variable (“assisting with calving and/or abortion in the last 12 months”) as GLMM did not converge when included separately in the final model. In the univariate analysis, people in direct contact with seropositive cattle and buffalo had 1.85 (0.98 to 3.50) times the odds of testing seropositive for Brucella spp. compared to those that did not (S5 Table). A biologically plausible interaction between farm status and association between assisting with calving/abortion and brucellosis status was investigated. However, only 5 people (5.7%) that did not assist with calving/abortion were seropositive (1 from a seropositive farm and 1 from a seronegative farm). As there were two few observations to include an interaction term between these variables, a new variable with three categories was created: i) assisted with calving and/or abortion in the last 12 months and from a farm classified as seropositive, ii) assisted with calving and/or abortion in the last 12 months and from a farm classified as seronegative and iii) have not assisted with calving or abortion in the past 12 months. Persons assisting with calving/abortion on a farm with seronegative livestock and people which did not assist with calving/abortion had 0.35 (95% CI: 0.17 to 7.1) and 0.21 (0.09 to 0.46) times the odds of testing seropositive compared to persons assisting with calving/abortion in a seropositive farm in the last 12 months. See S1 Text for full details on variables collected for human survey.

Table 8

Final multivariate model for all variables associated with Brucella seropositivity in people in direct contact with livestock, including farm status, using logistic regression with village included as a random effect.

Variable	Odds ratio
Assist with calving/abortion and farm status
Assist with calving/abortion on a farm with seropositive livestock	1	-
Assist with calving/abortion on a farm with seronegative livestock	0.35 (0.17 to 0.71)	<0.001
Do not assist with calving/abortion	0.21 (0.09 to 0.46)	<0.001
Age
Up to 30	1	-
31 to 40	1.10 (0.51 to 2.37)	0.82
>40	0.55 (0.26 to 1.15)	0.11

Discussion

To our knowledge, this is the first epidemiological study of Brucella spp. in India where human and livestock populations in direct contact are studied concurrently, and one of only a few carried out in Brucella endemic settings [23-26]. The results suggest that brucellosis is endemic at high levels in cattle and buffalo of rural Ludhiana district in Punjab with animal-level seroprevalence estimated to be 15.1% (95% CI: 15.9 to 19.8). Two previous surveys conducted using random sampling in Punjab State more than 10 years ago estimated true-seroprevalence to be 11.2% and 17.8% [9,27]. Both these studies used indirect ELISA’s with sLPS antigens, one on milk and one on serum. Further, approximately a third of dairy farms had at least one animal testing seropositive in this study.

This study also demonstrated that those in direct contact with large ruminants, either through their occupation or family herd, have a high risk of exposure to Brucella spp. with 9.7% of those screened testing seropositive. In a ‘general population’ survey of brucellosis where households in the same villages as the current study were randomly sampled (regardless of cattle ownership), 2.2% of persons screened were seropositive for Brucella spp. [22]. Risk factors for Brucella spp. seropositivity in this group were assisting with calving/abortion (61% of rural households kept cattle/buffalo) and consumption of goats’ milk. Another previous study in Punjab screened veterinarians, veterinary pharmacists and animal handlers working for the Department of Animal Husbandry for Brucella spp. and found 21.9% had a positive RBT result, 24.0% had a positive STAT result, 19.7% had a positive IgM ELISA result, and 53.8% had a positive IgG ELISA result [12]. Although Proch et al. (2018) did not use probabilistic sampling, this further supports the notion that persons in direct contact with livestock in Punjab are at high risk of exposure to Brucella spp. Taken together, these results indicate that the disease poses a significant burden in rural Ludhiana, particularly in high risk occupations. The primary risk factor for testing seropositive for Brucella spp. in this survey was assisting with calving or abortion; cattle and buffalo infected with Brucella spp. excrete high concentrations of the organism in placental membranes and aborted foetuses. This effect depended on farms status; people that assisted with calving/abortion on a farm with seronegative livestock and people that did not assist with calving/abortion had 0.35 (95% CI: 0.17 to 7.1) and 0.21 (0.09 to 0.46) times the odds of testing seropositive compared to persons assisting with calving/abortion in a seropositive farm in the last 12 months. This strongly supports the existence of a relationship between assisting with calving and abortion of seropositive cattle and infection. The results of this survey, supported by the parallel survey in the general population suggest that there is limited exposure of Brucella spp. to people via milk and dairy products produced by large ruminants. This is likely due to the common practice of always boiling milk before consumption (85.1% in this survey) or as part of processing it into other dairy products. Given that approximately 20% of those that assisted with calving or abortion in a positive farm had evidence of exposure to Brucella spp., it is likely that transmission via this route is more important than the foodborne route in the study population. India has the largest bovine and second largest human population in the world. Although these results cannot be extrapolated, if the situation is similar in other Indian states producing high volumes of bovine milk, transmission of B. abortus via direct contact may be responsible for a significant burden of brucellosis infections in people.

Counterintuitively, farms that did not always disinfect after calving were less likely to test seropositive. It is unlikely that this is a true association and may be due to some unmeasured confounding as separation was more common in intensive dairy farms. Disinfection is only possible on certain surfaces, e.g. concrete floors, and it may be that animals which calved on surfaces that could not be disinfected (e.g. outdoors) were further away from the rest of the herd than animals which calved on surfaces where disinfection was possible. Husbandry was very similar in the majority of farms visited, which may explain the lack of other associated farm-level management factors.

Study limitations

The study had several potential limitations. Only those persons having direct contact with livestock that were present at the time of the visit and gave informed consent were screened. However, eligible people were not formally enumerated nor refusals documented due to concerns farm workers may feel pressured by veterinary officers or their employers to take part. This could potentially bias the estimate if the characteristics of persons coming forward systematically differed from those who were not screened. However, this was considered unlikely given that people were not aware of brucellosis as a disease in humans and at least one person was recruited in the majority (87%) of farms using this passive recruitment method. Given the small herd sizes in the study area, it was often the case that one person was primarily responsible for the care of the animals, hence, only one person was recruited in the majority of farms. Only lactating animals were sampled, therefore male animals (which may play a role in disease transmission) were not tested, however, most farms were not keeping male livestock for breeding.

The S19 vaccine can interfere with serological tests [28]. Of the 28 animals vaccinated, 7 tested seropositive and it is unknown whether these were from infection (either prior to S19 conjunctival vaccination or because the vaccine does not confer full protection). Whether or not animals were vaccinated was not retained in the final model. If these animals were removed from the analysis then the animal seroprevalence results would reduce from 15.1% to 14.7% and farm-level seroprevalence would reduce from 32.9% to 32.2%. In addition, running logistic regression with vaccinated animals/farms removed led to the same variables being retained in the models. Therefore, if these were false positive results they do not appear to be biasing the results.

In the current study and the study by Proch et al. (2018) a large proportion of persons seropositive by IgG ELISA did not test positive by RBT. The study in the general population also found poor agreement between the diagnostic test results in humans (Mangtani et al., 2020). This suggests there may be issues with the specificity of the ELISA used in these surveys, However, the results of the cross-sectional study and the parallel survey described in Mangtani et. al. (2020) show heightened exposure in persons in direct contact with livestock in the sampled dairy farms (9.7%) vs. general rural population (2.2%) and the risk factors identified correspond with the known biology of the disease, suggesting that misclassification due to low test performance may not have been an issue. Significant issues with the specificity of the iELISA would have likely resulted in more positive results in unexposed groups (false positives) than were observed. For example, of the 167 people who did not currently assist with calving and abortion, but had direct contact with livestock, only 5 (2.3%) were seropositive. Of these five people, three reported assisting with calving or drinking raw milk in the past. Compared to the superior situation for diagnosis in animals, there is a distinct lack of standardisation of serological reagents for diagnosis of human brucellosis.

Recommendations

The best strategy for preventing introduction into negative dairy farms would be to screen new purchases, however, only four livestock owners reported doing this. The feasibility of this strategy depends on availability of screening tests for brucellosis at village level and farmers’ capacity to afford this. According to this survey around a third of dairy farms already had at least positive animal in their herd, therefore, management strategies to reduce within-herd transmission of Brucella spp., particularly when animals are calving may be more effective. Although the majority of farms did report separating livestock at the time of calving (62.5%), they are often still in close proximity to the rest of the herd and are returned to the herd very soon after.

At the time of this study a programme termed ‘Brucella free village’ was launched by the Department of Biotechnology with the mandate to “control and eradicate brucellosis in animals through stamping out of positive animals, vaccination of animals and maintaining sanitary conditions”. The initiative is planned to be piloted across 50 villages in 10 states, including Punjab. The programme is very comprehensive planning to combine intensive screening, segregation of positives and vaccination. Although this will lower disease frequency in the selected villages there are concerns with the feasibility of upscaling this programme. The majority of the villages in this study would be classified as ‘highly endemic’ according to this programme’s classification (prevalence >5%). The programme plans to isolate positive cows outside the village and slaughter positive buffalo. Given that almost 1 in 6 animals were seropositive in the cross-sectional survey, this strategy would require huge resources in this setting and may not be feasible unless prevalence was first sustainably lowered through methods such as vaccination.

A workshop was held at PGIMER campus on 21`November 2016 between State-level livestock, public health and food-safety actors around the theme of brucellosis control. The main objectives of the workshop were to ascertain what is being done to control brucellosis in Punjab State, identify opportunities to improve control, share project results with stakeholders and facilitate intersectoral communication. Nineteen participants attended the meeting, representing the main institutions involved in zoonosis control in Punjab, including Departments of Animal Husbandry, Dairy Development and Health and the Food and Drug Administration. In addition, public and animal health researchers, clinicians and representatives from the private sector (cooperatives and milk union) were present. Participants agreed that brucellosis was an important animal and public health disease in the state. However, despite vaccination policies in place low coverage is achieved, this is supported by the findings of this study. Stakeholders attributed low coverage lack of vaccine availability and safety concerns of veterinarians and farmers. Therefore, these issues would need to be addressed before any vaccination campaigns are implemented.

As people are primarily infected via animal sources, control in livestock (primarily ruminants) is key to reducing the public health impact of the disease. However, whilst the seroprevalence remains high, it is recommended that the Dept. of Animal Husbandry works with health authorities to design public health campaigns which could reduce the risk of human exposure to brucellosis. Most people sampled said they were not aware of brucellosis as a disease in humans (93.4%). Further, through discussions during fieldwork and the stakeholder workshop, it seems that rural healthcare workers are also unaware of the disease. Therefore, it is likely that brucellosis goes unrecognised or misdiagnosed at the level of primary healthcare centres. Awareness was created in the sampled villages, however, there is a need to disseminate this information further. The issue of human diagnostics was also discussed during the workshop and clinicians raised highlighted the need to include brucellosis in the guidelines for investigating pyrexia of unknown. A technical expert group on human brucellosis was established as an output from the stakeholder workshop. This group is developing best practice guidelines for treatment and diagnosis and fostering awareness in the public health sector. Further as a result of this survey, samples are being sent to GADVASU from a tertiary hospital for screening.

As the primary risk factor was assisting with calving/abortion, public health campaigns should also include recommendations regarding the use of personal protective equipment (PPE) when assisting with calving. The final questionnaire for persons in direct contact with livestock did not contain questions on the use of PPE as results of the pilot surveys and experience of the field team suggest it is extremely rare for PPE to be worn whilst milking or assisting with parturition. This was observed during fieldwork and lack of PPE has been described in other brucellosis endemic settings [29-31]. Therefore, consideration as to whether this is a realistic strategy is needed. In addition to reducing the risk of exposure to the individual, appropriate hygiene and management of animals during calving can also reduce within-herd transmission of the disease in livestock. Clear messages backed by scientific evidence as to what farmers can do to protect themselves, their workers and their livestock from brucellosis are needed. These should be informed by assessments considering cost, feasibility and acceptability. Veterinary officers working in villages and the university extension services, which hold farmers’ fayres and veterinary camps, are best placed to disseminate this knowledge. However, it is important that smallholders also receive these messages.

Conclusions

Brucellosis is endemic at high levels in cattle and buffalo in the study area and the population is at risk of infection via direct contact. Appropriate control strategies to reduce production losses in livestock and prevent infections in those exposed to these livestock are needed.

Univariate analysis to identify factors associated with Brucella spp. seropositivity at animal-level.

(DOCX)

Click here for additional data file.

Association between demographic variables and Brucella spp. seropositivity in people in direct contact with large ruminants using univariable logistic regression models with village included as a random-effect.

(DOCX)

Click here for additional data file.

Association between livestock contact and Brucella seropositivity in in people in direct contact with large ruminants using univariable logistic regression models with village included as a random-effect.

(DOCX)

Click here for additional data file.

Association between dairy consumption and Brucella seropositivity in people in direct contact with livestock using univariable logistic regression models with village included as a random-effect.

(DOCX)

Click here for additional data file.

Association between data from livestock testing and farm questionnaire and Brucella seropositivity in people in direct contact with livestock using univariable logistic regression models with village included as a random-effect.

(DOCX)

Click here for additional data file.

Questionnaire used to interview persons in direct contact with livestock in the studied dairy farms.

(DOCX)

Click here for additional data file.

20 Apr 2020

Dear Miss Holt,

Thank you very much for submitting your manuscript "Epidemiology of brucellosis in cattle and dairy farmers of rural Ludhiana, Punjab" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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Sincerely,

Claudia Munoz-Zanzi

Associate Editor

PLOS Neglected Tropical Diseases

Hélène Carabin

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: The creation of variables for analysis is unplanned. Many variables could have been merged to reduce the number of variables. The list/number of variables used to build models should be explicitly presented.

Be consistent with the use of terms infected, positive and seropositive. Avoid using them interchangeably. Please double check everywhere in the manuscript.

East Ludhiana has the largest population: of animals or humans?

It is not clear why eight herds were selected in each village.

Serological testing: Do the serological tests used in the study detect antibodies against Brucella abortus or any Brucella spp?

No results for SAT testing have been provided. Please clarify.

It is not clear if the same samples were retested or repeated samples were obtained from animals. The interpretation of results from these two scenarios would be different. Three sentences from different sections with different meanings are copied below.

“All seropositive samples and a proportion of randomly selected negative samples were retested

using the same procedure.”

“Positive samples from livestock were retested and livestock were classified as positive if two

positive test results were obtained. People were classified as seropositive if they tested positive

to either the Rose-Bengal test or IgG ELISA.”

“Of the 1854 cattle and buffalo sampled, 280 tested positive on two consecutive milk ELISA’s,”

Change the sentence “Univariate analysis was performed” to “Univariate logistic regression analyses were performed”

Similarly, specify the model used for multivariable analyses: generalised linear mixed models (GLMM).

ICC: ICC can be calculated from GLMM, so you don't need to conduct ANOVA. Please refer to Vet Epi Research by Ian Dohoo, if not sure.

“Age was included as an a priori confounder” It is not clear if the human or animal age was included. Also, it is not clear in which model age was included as a confounder.

Were the assumptions of the model evaluated? If yes, how? Were the assumptions met?

Did you test interactions between significant variables in the model? It became apparent from the last model that the interactions were not tested. Please justify.

Did you test for potential collinearity between explanatory variables? If yes, how?

Reviewer #2: Please see attached document for comments.

Reviewer #3: -Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

The objectives are given and are mostly clear. The third objective regarding human exposure risks could be more clearly stated (e.g. estimate prevalence and identify risk factors as for other populations rather than “exposure… was investigated” which is quite vague)

There is quite a lot of content in the results and more so in the discussion that is not very clearly aligned to these objectives so more focus and structure in the reporting of these sections can help improve this.

-Is the study design appropriate to address the stated objectives?

The design reported sounds appropriate but some details are missing.

For example

Can you clarify/explain your dairy farm definition – were farms keeping 4 cows AND/OR 4 female buffalo included? – you specify “OR” only currently but I think included mostly farms keeping both species.

In the reporting of the multi stage selection can you reorder the content on Tehsil selection so that you explain ‘how’ selection was done at the beginning of section.

You report ‘(Jagraon, Payal and Samrala)’ here without any explanation of what these are.

The order of these section is not very clear. The sample size content could be moved to the beginning or end but appearing in the middle of the description of selection steps is quite confusing. Can you explain how you chose the value of 6% precision for this analysis as that’s not a standard default value?

Can you give an indication of the denominators in each step? E.g. is 8 dairy farms per village a large of small % of the total? You say ‘up to eight dairy farms” were selected. When and why was this less than 8?

If the farm had > 9 animals (NB – is this > 9 animals total or > 9 lactating females?) you report that a systematic sample was taken. How many animals is this? What criteria were sampling decisions made on? Was this really systematic sampling or convenience? Either is defendable – but it needs to be clear. The relevance of the statement re tethering is not clear.

Can you give more rationale for the repeat testing approaches used and present the data on test repeatability for the different populations and tests in the results. Please report the results of the ‘validation’ of RBT antigens also – this can be in the SI but are important data to make accessible for evaluation.

The level of detail given for different tests is variable – e.g. you report the cu-off rationale and rules for the BRUCELISA but not the DEMEDITEC test so this should be standardised (and explain how the kit defines inconclusives)

Please give more detail on the performance, results and guidance based on any SATs on human samples.

The exact questions asked of respondents and the period of reference for the questions about risk behaviours should be included in the methods (the survey tool could be included as an SI file).

-Is the population clearly described and appropriate for the hypothesis being tested?

The rationale for combining cattle and buffalo in several design and results reporting steps is not clear. What was the prevalence in each species? In the reporting of the model results the presence of different species and herd composition appear to be important but the detail on these metrics is not given or discussed very clearly.

The composition of the human population reported on should be clearer. Can you give a clearer description of how this population was defined? Did you record data on the proportion of eligible individuals that ‘accepted’ the offer of screening? If not, can you estimate this? Assuming < 100% uptake what biases might this introduce and how does that affect your interpretation of the findings?

In all of the tables and figures please include ensure that the population (animals, people or herds/farms) is clearly and simply stated each time – e.g. tables S5 and S6 are presumably about serostatus in humans but that isn’t stated.

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

This calculation can be more clearly described. The content in the main paper is not very informative and I’d recommend stating the values currently reported in the SI in the text here so that all of the assumptions of the calculation are given up front. Please include a statement in the text of the sample size that the calculation indicated required (and then in the results, explicit statement that this was achieved)

-Were correct statistical analysis used to support conclusions?

The description and presentation of many of the stats and findings is often very descriptive and quite repetitive. You also report GLMM analyses that sound entirely appropriate for this dataset but then revert back to presentation and interpretation of raw data over the model outputs.

This section would be clearer if reordered. Start with clear description of the populations and outcomes modelled. I think that a common approach was taken to model development for the 3 different populations. If so, compile all of the descriptions in one place and state this clearly. The description of the forward model building is not very clear. What order were variables evaluated and added in? Can you include references to support the decisions made based on coef and SE changes etc.

You state that variables with a p<0.2 in univariable analyses were evaluated in multivariable models but its not clear what test the p value(s) mentioned are related to? If one level of a multifactor variable has a p for the coef estimate > 0.2 does that variable go through? Why were LRTs not used to evaluate the contribution to model fit of the variables as a whole?.

I think all models are logistic regressions with binomial errors but this is not clearly described (except in Table 3 legend).

Many apparently continuous variables are categorised for model analyses and its not clear why? E.g. number of animals in different groups. Please explain or update as this seems to be an inefficient use of data. This also appears to change – in Table 3 the number of cows variable appears to be continuous?

Can you explain justification of manual calculation of ICC metrics rather than reporting and using metrics derived from the random effects estimated in the models. Even if not used this way, all summaries of the GLMMs should include summary of the random effects estimated.

Can you explain why the farm/herd RE was not included in the model of human serostatus data? Was this evaluated? This is presumably as you had many farms with 1 observation but should be stated clearly.

-Are there concerns about ethical or regulatory requirements being met?

There is a mention in the methods that human serum agglutination tests were performed but no results are given on this. Were any presumptive cases identified? What case definition was used for this step and what procedures were followed for data sharing and clinical advice for individuals who were tested – and then received positive or negative results?

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Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: ALL TABLES:

- Please report overall p-values for each variable. These p-values should be used to make decisions about whether or not a variable is significant, instead of the p-values currently presented.

- Odds ratio for the reference category should be 1. Please replace ‘-‘ with 1 in all tables.

Table 1

- Not sure why cows and buffalo are two different variables. The variable herd type adequately captures this information. retain herd type and delete the other two variables.

- What is the reason for testing the variables cow and buffalo calves? Justify or exclude.

- Is there any reason for including number of cows and buffaloes separately, in addition to the variable total herd size? Justify or exclude (i.e. just retain total herd size).

Table 2

What is LR? Please explain the abbreviation in a footnote to the table.

Insemination cows, insemination buffalo, natural service: why are there three variables instead of one? The variable should be ‘type of insemination: natural or AI’

Table 3

- Please see my comment about overall p-values above.

- Why are the odds ratios for Sub-district very different from those of univariable results? For example, the odds of Samarala are nearly double than that of East Ludhiana in Table 1 and nearly half of East Ludhiana in Table 3. Please double check to make sure that you are using the same categories as in Table 1. Note that the odds ratios can change after the addition of other variables in the model, but such a drastic change needs to be investigated (e.g. the variables in the final model may be collinear). Also, the herd prevalence estimates in Table S2 do not seem to align with the estimates in Table 1. Please review these data and results.

- I assume that the ‘number of cows’ variable was used as a numeric variable. Did you test whether its association with the logit of the outcome linear?

- Add the reference category for the last variable in Table 3.

Table S3:

- Why are there two variables for ‘role on farm’ and religion?

- It doesn’t seem appropriate to combine Muslims with Hindus just because their numbers are small. The proportions seropositive were actually similar in Sikh and Hindus, so you are going to make incorrect conclusions by clubbing Hindus with Muslims.

Table S4:

- It is not clear why odds ratios and p-values have been suppressed for some variables.

- Why are there two variables for each of the following variables: ‘milking LR’, ‘assisting with calving’, ‘assisting with abortion?

-

Table 4

- Looks like age is used as a numeric variable here. Did you test the assumption of linearity for age?

- In contrast, in Table S4, age was included as a categorial variable. Please be consistent. Why was age categorised for univariable analyses and then used as numeric for multivariable analyses?

Table 5 is unnecessary. Just briefly present the results in the text.

Table 6

- Combining never with the past category is not advisable.

- Which variables were tested in this model?

- Why not test if the interactions were significant, instead of arbitrarily combining variables? Delete Tables 8 and 9 and include the interaction term in Table 6 itself.

Section 2.1.2

- Please report ICC values for other models.

Table 6: Can you please clarify which variables were included in the model presented in Table 6? Were all the variables in tables from S3 to S5 tested in this model?

“Angandwadi workers”: Briefly describe who they are. Are they healthcare workers or medical practitioners?

Discussion, second last paragraph: Please define PPE.

Discussion, last paragraph: “Given the absence of PPE use in the current survey”. I might have missed but didn't notice this finding in the current study. Please clarify.

Reviewer #2: Please see attached document for comments.

Reviewer #3: -Does the analysis presented match the analysis plan?

The ordering of the results could be more clearly mapped to the study objectives – e.g. consistent ordering of animals, herds and humans. The livestock results sections opens with a description of milk consumption patterns which aren’t really mentioned before this point. Similar the dairy farms section is focused on the small n farms that kept goats. This is a confusing point to lead with as its not focused on any of the stated objectives, the data are very sparse and none of the results presented are informed by the model analyses performed. Several associations are reported without an explicit statement of (or reference to the table) the results from the analyses presented that support each statement.

Its not clear why the data for cattle and buffalo are reported together vs by species.

Much of the reporting of the univariable analyses can be dropped so that the results are focused on the multivariable model findings. All of the univariable results are given in the tables (I think) but these are not consistently presented. Some include indications of the variables evaluated in multivariable models but not all. The explanation of many variables is unclear – does Buffalo Yes/No mean indicator of species presence on the farm?

There are several cases where a set of correlated variables are evaluated and its not clear how decisions were made on which variable to carry through to multivariable analysis? E.g. were model presence of buffalo and herd type fitted in the same models?

-Are the results clearly and completely presented?

The levels of many factors are not really explained. Its also important to explain clearly how some of the factor levels are defined. Its not clear from the info given that the decision to combine Never and past and compare with “Yes” is a justifiable choice. What are the time periods for each level? Why

Do you need to present both model 6 and 7? Why is age retained in the model?

In some of the results test sections the results focused on are not very obviously focused on the objectives of the paper and involve quite a lot of anecdotal description of data, to suggest associations that were either not statistically evaluated or are not support by the models presented – e.g. symptom reporting in occupationally exposed period. Given the very low diagnostic value of clinical signs, use of serostatus vs clinical illness as the outcome, long persistence of Ab and retrospective nature of the survey there is no very strong reason to evaluate evidence of any association. These data are not mentioned elsewhere in the methods or results.

You describe a slight increase of seroprevalence with age based on the figure but as the models indicate no association I don’t its justifiable to present this as an increase with age.

Quite a lot of the descriptive text on e.g. milk consumption practices could be replaced by referring to the detail on these data (and the model evaluations performed) in a table (s6) rather than brief text summary.

The interpretation of the data on risk factors for human exposure is complicated by the lack of clarity on the population sampled and the way the variables were evaluated. The rationale for the descriptive analysis of association between different explanatory variables is not clear. Why weren’t these effects evaluated using interaction terms? The nature of the comparator population in reporting of the increased risk in people with occupational exposure to seropositive animals is not clear. Its also unclear why a new model is used to evaluate this variable/interaction vs including the interaction in the full model for this population?

-Are the figures (Tables, Images) of sufficient quality for clarity?

In the first figure can you update the legend to explain the overlap shading or update the plot so that the data for the sexes are presented adjacent but not overlapping.

In the tables, more detail on the variables and levels presented are needed.

What is “LR”?

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Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: The conclusions will change after analyses are repeated after finalising the variables and their categories.

Reviewer #2: Please see attached document for comments.

Reviewer #3: The correspondence in terms of structure and key points addressed between the discussion/conclusion and the rest of the paper is not very strong

Some of the key findings are not really discussed – why do you think there is such a strong effect of region on serostatus? Why might this be? What is your interpretation of the ICC data presented?

There is quite a lot of content in the discussion about a sister paper that is not yet published. Its not really appropriate either present findings from another study here or to include reference to the other study until the other paper can be cited.

-Are the conclusions supported by the data presented?

The speculation about infecting species of Brucella is not supported by any of the data presented in this paper and I would not include this for that reason

Some of the points about relative importance of different routes of transmission are interesting but don’t make as good use of the available data as is possible to support these points.

The description of the counterintuitive finding is a little speculative. The argument that people might take more precautions during parturition if aware that there was brucellosis in their herd is somewhat undermined by the low levels of awareness of the disease that is presented elsewhere?

The content on possible control, surveillance and transmission prevention strategies is interesting and important but is currently mentioned in a few different places and is not really linked to any of the data presented or clearly described in the context of options under consideration in this setting. What control options might be feasible in this context? How do the data presented here help guide thinking about these options? There is quite a lot of narrative content presented in this section describing findings from other studies (e.g. on PPE) that don’t currently add a lot to the interpretation of the findings from this study.

-Are the limitations of analysis clearly described?

There is not an explicit section on study limitations

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

The public health relevance of the findings is mentioned but the content is quite vague and non-specific. The general points about the challenges of vaccination are not really the findings of this study. Can you include more focused content on what the findings of this study tell us and how they could be used to guide or focus next steps research or pollcy?

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Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: Abstract

Change “The aim of this study is to” to “The aim of this study was to”

Author summary

Reword this sentence: “This study to estimate the frequency of exposure to the bacteria

(Brucella spp.) in dairy animals in Ludhiana district in Punjab State of India and persons in

contact with these livestock either through their occupation or household.”

Introduction

Replace comma with a period: India is currently the world’s leading milk producer. This study was conducted…

Reword: produces the most cattle and buffalo’ milk…

Reword or split: Endemic and emerging livestock diseases pose a threat to the milk industry due to reduced productivity of livestock (direct impact), trade barriers and, if a milkborne zoonosis, posing a

threat to consumer health.

Corbel, 1997: Try to use primary sources if available.

Reword or split: However, many of the previous studies have not used

probabilistic sampling therefore the estimates may be biased, an example of `one study where

sampling was unbiased was conducted over 10-years ago and reported a lower animal-level

seroprevalence estimate.

Reword or split: Incidence of the disease in

humans relates to the frequency of brucellosis in the local livestock population it is an

occupational zoonosis for farmers and veterinarians

Reword or combine: Only one sample was positive by

RBT but negative by IgG ELISA. Whereas 40 samples were positive IgG ELISA and negative by

RBT.

Split into two sentences: Only 24 (1.3%) animals in four farms were vaccinated against Brucella spp. this was mostly using the reduced

Reword or combine: The majority of the participants were male (69.1%) and over 20 (92.4%). With family members accounting for the majority of people in occupational contact with cattle on dairy farms (56.9%), with around owners accounting for 23.2% and employees accounting for 19.9%.

Reviewer #2: (No Response)

Reviewer #3: Please include page and line numbers for future submissions as that greatly improves the review process

There are several places where the terminology is inconsistent which creates ambiguity and confusion. Try to use a stripped back set of terms consistently - e.g. herds vs dairy farms vs farms, the descriptions of the various human populations need clarification throughout (people present ‘involved with husbandry’, household members, family members, farm workers, occupationally exposed group etc are used at various points and its not clear how these all relate to each other).

There are acronyms that need to be checked for full explanation at first use also.

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: This study was conducted to estimate the prevalence of brucellosis in cattle, buffalo and in-contact humans and to identify the risk factors for seropositivity. The manuscript has some novel data about brucellosis epidemiology in India. The authors should be commended for using a robust design for the study, in particular, for using probabilistic sampling methods to select villages and animals. However, the casual approach to data analysis and writing the manuscript was disappointing and could have been improved. The manuscript has many grammatical errors, particularly, in the first two sections. I have listed some of the issues above, but the authors should read the manuscript thoroughly before resubmission. The manuscript should be of interest to the journal audience, but the authors would have to rebuild most of the models and thoroughly revise the manuscript before it could be accepted for publication.

Reviewer #2: Please see attached document for comments.

Reviewer #3: Overall this paper presents data from an interesting study in a context where data on brucellosis are limited. These data have definite value but are currently under-utilised and the presentation of the key findings from this work is not as clear as it should be.

Some updates to the structure of the article can greatly increase the focus and clarity of the work presented. There are also updates needed to the presentation of the statistical analyses (see notes above).

--------------------

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Submitted filename: PNTD 20-00277 Comments.docx

Click here for additional data file.

14 Oct 2020

Submitted filename: PNTD 20-00277 Comments Final.docx

Click here for additional data file.

4 Jan 2021

Dear Miss Holt,

We are pleased to inform you that your manuscript 'Epidemiology of brucellosis in cattle and dairy farmers of rural Ludhiana, Punjab' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Claudia Munoz-Zanzi

Associate Editor

PLOS Neglected Tropical Diseases

Hélène Carabin

Deputy Editor

PLOS Neglected Tropical Diseases

***********************************************************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: Yes. The authors have appropriately revised the manuscript based on reviewers' suggestions. I think Table 1, 6 and 7 can be presented as supplementary materials but I will let the editor make a final decision.

Figure 1 can also be presented as supplementary material. Regardless, I don't think it is appropriate to call it a causal diagram. I would be better to refer to it as a 'conceptual diagram'.

Reviewer #2: Holt et al., described the epidemiology of animal and human brucellosis on dairy farms in rural Ludhiana, Punjab, India. The manuscript is well-written, and results are presented clearly and precisely. The findings from their study suggested a high seroprevalence of the disease in cattle, buffalo, and animal workers on farms in Punjab, as well as the significance of occupational exposure to the disease in this region. Hence, advocating for control and preventive strategies against production losses in animals and Brucella infection in animal workers.

The authors made all the requested changes and corrections which have significantly improved the manuscript.

**********

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Yes. The authors have revised the manuscript based on our suggestions.

Tables 6 and 7 can be presented as supplementary materials but I will let the editor make a final decision.

Reviewer #2: Yes

**********

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: Yes.

Reviewer #2: Yes

**********

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: Accept.

Reviewer #2: (No Response)

**********

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: The manuscript has substantially improved after revisions. Thank you to the authors for considering our suggestions and revising the manuscript.

Reviewer #2: The authors did a great job articulating the limitations of the study. They also provided pertinent recommendations for the effective control of brucellosis in rural Ludhiana, Punjab, which are potentially applicable to other endemic regions.

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

15 Mar 2021

Dear Miss Holt,

We are delighted to inform you that your manuscript, "Epidemiology of brucellosis in cattle and dairy farmers of rural Ludhiana, Punjab," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly.

Soon after your final files are uploaded, the early version of your manuscript will be published online unless you opted out of this process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases